diff --git a/CMakeExternals/MITKData.cmake b/CMakeExternals/MITKData.cmake index 601ccdbf9a..46bf725e21 100644 --- a/CMakeExternals/MITKData.cmake +++ b/CMakeExternals/MITKData.cmake @@ -1,39 +1,39 @@ #----------------------------------------------------------------------------- # MITK Data #----------------------------------------------------------------------------- # Sanity checks if(DEFINED MITK_DATA_DIR AND NOT EXISTS ${MITK_DATA_DIR}) message(FATAL_ERROR "MITK_DATA_DIR variable is defined but corresponds to non-existing directory") endif() set(proj MITK-Data) set(proj_DEPENDENCIES) set(MITK-Data_DEPENDS ${proj}) if(BUILD_TESTING) - set(revision_tag fee70ea2) # first 8 characters of hash-tag + set(revision_tag 7968c5c0) # first 8 characters of hash-tag # ^^^^^^^^ these are just to check correct length of hash part ExternalProject_Add(${proj} SOURCE_DIR ${proj} # GIT_REPOSITORY https://phabricator.mitk.org/diffusion/MD/mitk-data.git # GIT_TAG ${revision_tag} URL ${MITK_THIRDPARTY_DOWNLOAD_PREFIX_URL}/mitk-data_${revision_tag}.tar.gz UPDATE_COMMAND "" CONFIGURE_COMMAND "" BUILD_COMMAND "" INSTALL_COMMAND "" DEPENDS ${proj_DEPENDENCIES} ) set(MITK_DATA_DIR ${CMAKE_CURRENT_BINARY_DIR}/${proj}) else() mitkMacroEmptyExternalProject(${proj} "${proj_DEPENDENCIES}") endif(BUILD_TESTING) diff --git a/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkDiffusionCoreIOMimeTypes.cpp b/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkDiffusionCoreIOMimeTypes.cpp index 5763b03118..37bb1546d9 100644 --- a/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkDiffusionCoreIOMimeTypes.cpp +++ b/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkDiffusionCoreIOMimeTypes.cpp @@ -1,630 +1,642 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkDiffusionCoreIOMimeTypes.h" #include "mitkIOMimeTypes.h" #include #include #include #include #include #include #include #include #include namespace mitk { std::vector DiffusionCoreIOMimeTypes::Get() { std::vector mimeTypes; // order matters here (descending rank for mime types) mimeTypes.push_back(DWI_NRRD_MIMETYPE().Clone()); mimeTypes.push_back(DWI_NIFTI_MIMETYPE().Clone()); mimeTypes.push_back(DWI_FSL_MIMETYPE().Clone()); mimeTypes.push_back(DWI_DICOM_MIMETYPE().Clone()); mimeTypes.push_back(DTI_MIMETYPE().Clone()); mimeTypes.push_back(ODF_MIMETYPE().Clone()); mimeTypes.push_back(PEAK_MIMETYPE().Clone()); mimeTypes.push_back(SH_MIMETYPE().Clone()); return mimeTypes; } // Mime Types DiffusionCoreIOMimeTypes::DiffusionImageNrrdMimeType::DiffusionImageNrrdMimeType() : CustomMimeType(DWI_NRRD_MIMETYPE_NAME()) { std::string category = "Diffusion Weighted Images"; this->SetCategory(category); this->SetComment("Diffusion Weighted Images"); this->AddExtension("dwi"); //this->AddExtension("hdwi"); // saving with detached header does not work out of the box this->AddExtension("nrrd"); } bool DiffusionCoreIOMimeTypes::DiffusionImageNrrdMimeType::AppliesTo(const std::string &path) const { bool canRead( CustomMimeType::AppliesTo(path) ); // fix for bug 18572 // Currently this function is called for writing as well as reading, in that case // the image information can of course not be read // This is a bug, this function should only be called for reading. if( ! itksys::SystemTools::FileExists( path.c_str() ) ) { return canRead; } //end fix for bug 18572 std::string ext = this->GetExtension( path ); ext = itksys::SystemTools::LowerCase( ext ); // Simple NRRD files should only be considered for this mime type if they contain // corresponding tags if( ext == ".nrrd" ) { itk::NrrdImageIO::Pointer io = itk::NrrdImageIO::New(); io->SetFileName(path); try { io->ReadImageInformation(); itk::MetaDataDictionary imgMetaDictionary = io->GetMetaDataDictionary(); std::vector imgMetaKeys = imgMetaDictionary.GetKeys(); std::vector::const_iterator itKey = imgMetaKeys.begin(); std::string metaString; for (; itKey != imgMetaKeys.end(); itKey ++) { itk::ExposeMetaData (imgMetaDictionary, *itKey, metaString); if (itKey->find("modality") != std::string::npos) { if (metaString.find("DWMRI") != std::string::npos) { return canRead; } } } } catch( const itk::ExceptionObject &e ) { MITK_ERROR << "ITK Exception: " << e.what(); } canRead = false; } return canRead; } DiffusionCoreIOMimeTypes::DiffusionImageNrrdMimeType* DiffusionCoreIOMimeTypes::DiffusionImageNrrdMimeType::Clone() const { return new DiffusionImageNrrdMimeType(*this); } DiffusionCoreIOMimeTypes::DiffusionImageNrrdMimeType DiffusionCoreIOMimeTypes::DWI_NRRD_MIMETYPE() { return DiffusionImageNrrdMimeType(); } DiffusionCoreIOMimeTypes::DiffusionImageNiftiMimeType::DiffusionImageNiftiMimeType() : CustomMimeType(DWI_NIFTI_MIMETYPE_NAME()) { std::string category = "Diffusion Weighted Images"; this->SetCategory(category); this->SetComment("Diffusion Weighted Images"); this->AddExtension("nii.gz"); this->AddExtension("nii"); } bool DiffusionCoreIOMimeTypes::DiffusionImageNiftiMimeType::AppliesTo(const std::string &path) const { bool canRead(CustomMimeType::AppliesTo(path)); // fix for bug 18572 // Currently this function is called for writing as well as reading, in that case // the image information can of course not be read // This is a bug, this function should only be called for reading. if (!itksys::SystemTools::FileExists(path.c_str())) { return canRead; } //end fix for bug 18572 std::string ext = this->GetExtension(path); ext = itksys::SystemTools::LowerCase(ext); // Nifti files should only be considered for this mime type if they are // accompanied by bvecs and bvals files defining the diffusion information if (ext == ".nii" || ext == ".nii.gz") { std::string base_path = itksys::SystemTools::GetFilenamePath(path); std::string base = this->GetFilenameWithoutExtension(path); std::string filename = base; if (!base_path.empty()) { base = base_path + "/" + base; base_path += "/"; } if (itksys::SystemTools::FileExists(std::string(base + ".bvec").c_str()) && itksys::SystemTools::FileExists(std::string(base + ".bval").c_str()) ) { return canRead; } if (itksys::SystemTools::FileExists(std::string(base + ".bvecs").c_str()) && itksys::SystemTools::FileExists(std::string(base + ".bvals").c_str()) ) { return canRead; } // hack for HCP data if ( filename=="data" && itksys::SystemTools::FileExists(std::string(base_path + "bvec").c_str()) && itksys::SystemTools::FileExists(std::string(base_path + "bval").c_str()) ) { return canRead; } if ( filename=="data" && itksys::SystemTools::FileExists(std::string(base_path + "bvecs").c_str()) && itksys::SystemTools::FileExists(std::string(base_path + "bvals").c_str()) ) { return canRead; } canRead = false; } return canRead; } DiffusionCoreIOMimeTypes::DiffusionImageNiftiMimeType* DiffusionCoreIOMimeTypes::DiffusionImageNiftiMimeType::Clone() const { return new DiffusionImageNiftiMimeType(*this); } DiffusionCoreIOMimeTypes::DiffusionImageNiftiMimeType DiffusionCoreIOMimeTypes::DWI_NIFTI_MIMETYPE() { return DiffusionImageNiftiMimeType(); } DiffusionCoreIOMimeTypes::DiffusionImageFslMimeType::DiffusionImageFslMimeType() : CustomMimeType(DWI_FSL_MIMETYPE_NAME()) { std::string category = "Diffusion Weighted Images"; this->SetCategory(category); this->SetComment("Diffusion Weighted Images"); this->AddExtension("fslgz"); this->AddExtension("fsl"); } bool DiffusionCoreIOMimeTypes::DiffusionImageFslMimeType::AppliesTo(const std::string &path) const { bool canRead(CustomMimeType::AppliesTo(path)); // fix for bug 18572 // Currently this function is called for writing as well as reading, in that case // the image information can of course not be read // This is a bug, this function should only be called for reading. if (!itksys::SystemTools::FileExists(path.c_str())) { return canRead; } //end fix for bug 18572 std::string ext = this->GetExtension(path); ext = itksys::SystemTools::LowerCase(ext); // Nifti files should only be considered for this mime type if they are // accompanied by bvecs and bvals files defining the diffusion information if (ext == ".fsl" || ext == ".fslgz") { std::string base_path = itksys::SystemTools::GetFilenamePath(path); std::string base = this->GetFilenameWithoutExtension(path); if (!base_path.empty()) base = base_path + "/" + base; if (itksys::SystemTools::FileExists(std::string(base + ".bvec").c_str()) && itksys::SystemTools::FileExists(std::string(base + ".bval").c_str()) ) { return canRead; } if (itksys::SystemTools::FileExists(std::string(base + ".bvecs").c_str()) && itksys::SystemTools::FileExists(std::string(base + ".bvals").c_str()) ) { return canRead; } if (itksys::SystemTools::FileExists(std::string(base + ext + ".bvec").c_str()) && itksys::SystemTools::FileExists(std::string(base + ext + ".bval").c_str()) ) { return canRead; } if (itksys::SystemTools::FileExists(std::string(base + ext + ".bvecs").c_str()) && itksys::SystemTools::FileExists(std::string(base + ext + ".bvals").c_str()) ) { return canRead; } canRead = false; } return canRead; } DiffusionCoreIOMimeTypes::DiffusionImageFslMimeType* DiffusionCoreIOMimeTypes::DiffusionImageFslMimeType::Clone() const { return new DiffusionImageFslMimeType(*this); } DiffusionCoreIOMimeTypes::DiffusionImageFslMimeType DiffusionCoreIOMimeTypes::DWI_FSL_MIMETYPE() { return DiffusionImageFslMimeType(); } DiffusionCoreIOMimeTypes::DiffusionImageDicomMimeType::DiffusionImageDicomMimeType() : CustomMimeType(DWI_DICOM_MIMETYPE_NAME()) { std::string category = "Diffusion Weighted Images"; this->SetCategory(category); this->SetComment("Diffusion Weighted Images"); this->AddExtension("gdcm"); this->AddExtension("dcm"); this->AddExtension("DCM"); this->AddExtension("dc3"); this->AddExtension("DC3"); this->AddExtension("ima"); this->AddExtension("img"); } bool DiffusionCoreIOMimeTypes::DiffusionImageDicomMimeType::AppliesTo(const std::string &path) const { itk::GDCMImageIO::Pointer gdcmIO = itk::GDCMImageIO::New(); bool canRead = gdcmIO->CanReadFile(path.c_str()); if (!canRead) return canRead; mitk::DICOMDCMTKTagScanner::Pointer scanner = mitk::DICOMDCMTKTagScanner::New(); mitk::DICOMTag ImageTypeTag(0x0008, 0x0008); mitk::DICOMTag SeriesDescriptionTag(0x0008, 0x103E); mitk::StringList relevantFiles; relevantFiles.push_back(path); scanner->AddTag(ImageTypeTag); scanner->AddTag(SeriesDescriptionTag); scanner->SetInputFiles(relevantFiles); scanner->Scan(); mitk::DICOMTagCache::Pointer tagCache = scanner->GetScanCache(); mitk::DICOMImageFrameList imageFrameList = mitk::ConvertToDICOMImageFrameList(tagCache->GetFrameInfoList()); mitk::DICOMImageFrameInfo *firstFrame = imageFrameList.begin()->GetPointer(); std::string byteString = tagCache->GetTagValue(firstFrame, ImageTypeTag).value; if (byteString.empty()) return false; std::string byteString2 = tagCache->GetTagValue(firstFrame, SeriesDescriptionTag).value; if (byteString2.empty()) return false; if (byteString.find("DIFFUSION")==std::string::npos && byteString2.find("diff")==std::string::npos) return false; // if (byteString.find("NONE")==std::string::npos) // return false; return canRead; } DiffusionCoreIOMimeTypes::DiffusionImageDicomMimeType* DiffusionCoreIOMimeTypes::DiffusionImageDicomMimeType::Clone() const { return new DiffusionImageDicomMimeType(*this); } DiffusionCoreIOMimeTypes::DiffusionImageDicomMimeType DiffusionCoreIOMimeTypes::DWI_DICOM_MIMETYPE() { return DiffusionImageDicomMimeType(); } DiffusionCoreIOMimeTypes::PeakImageMimeType::PeakImageMimeType() : CustomMimeType(PEAK_MIMETYPE_NAME()) { std::string category = "Peak Image"; this->SetCategory(category); this->SetComment("Peak Image"); this->AddExtension("nrrd"); this->AddExtension("nii"); this->AddExtension("nii.gz"); + this->AddExtension("peak"); } bool DiffusionCoreIOMimeTypes::PeakImageMimeType::AppliesTo(const std::string &path) const { + std::string ext = itksys::SystemTools::GetFilenameExtension(path); + if (ext==".peak") + return true; + + try { itk::NrrdImageIO::Pointer io = itk::NrrdImageIO::New(); if ( io->CanReadFile( path.c_str() ) ) { io->SetFileName( path.c_str() ); io->ReadImageInformation(); if ( io->GetPixelType() == itk::ImageIOBase::SCALAR && io->GetNumberOfDimensions()==4 && io->GetDimensions(3)%3==0) return true; } } + catch(...) + {} + + try { itk::NiftiImageIO::Pointer io = itk::NiftiImageIO::New(); if ( io->CanReadFile( path.c_str() ) ) { io->SetFileName( path.c_str() ); io->ReadImageInformation(); if ( io->GetPixelType() == itk::ImageIOBase::SCALAR && io->GetNumberOfDimensions()==4 && io->GetDimensions(3)%3==0) return true; } } + catch(...) + {} return false; } DiffusionCoreIOMimeTypes::PeakImageMimeType* DiffusionCoreIOMimeTypes::PeakImageMimeType::Clone() const { return new PeakImageMimeType(*this); } DiffusionCoreIOMimeTypes::PeakImageMimeType DiffusionCoreIOMimeTypes::PEAK_MIMETYPE() { return PeakImageMimeType(); } DiffusionCoreIOMimeTypes::SHImageMimeType::SHImageMimeType() : CustomMimeType(SH_MIMETYPE_NAME()) { std::string category = "SH Image"; this->SetCategory(category); this->SetComment("SH Image"); this->AddExtension("nii.gz"); this->AddExtension("nii"); this->AddExtension("nrrd"); this->AddExtension("shi"); } bool DiffusionCoreIOMimeTypes::SHImageMimeType::AppliesTo(const std::string &path) const { { itk::NrrdImageIO::Pointer io = itk::NrrdImageIO::New(); try { io->SetFileName( path.c_str() ); io->ReadImageInformation(); if ( io->GetPixelType() == itk::ImageIOBase::SCALAR && io->GetNumberOfDimensions()==4) { switch (io->GetDimensions(3)) { case 6: return true; break; case 15: return true; break; case 28: return true; break; case 45: return true; break; case 66: return true; break; case 91: return true; break; default : return false; } } } catch(...) {} } { itk::NiftiImageIO::Pointer io = itk::NiftiImageIO::New(); if ( io->CanReadFile( path.c_str() ) ) { io->SetFileName( path.c_str() ); io->ReadImageInformation(); if ( io->GetPixelType() == itk::ImageIOBase::SCALAR && io->GetNumberOfDimensions()==4) { switch (io->GetDimensions(3)) { case 6: return true; break; case 15: return true; break; case 28: return true; break; case 45: return true; break; case 66: return true; break; case 91: return true; break; default : return false; } } } } return false; } DiffusionCoreIOMimeTypes::SHImageMimeType* DiffusionCoreIOMimeTypes::SHImageMimeType::Clone() const { return new SHImageMimeType(*this); } DiffusionCoreIOMimeTypes::SHImageMimeType DiffusionCoreIOMimeTypes::SH_MIMETYPE() { return SHImageMimeType(); } CustomMimeType DiffusionCoreIOMimeTypes::DTI_MIMETYPE() { CustomMimeType mimeType(DTI_MIMETYPE_NAME()); std::string category = "Tensor Image"; mimeType.SetComment("Diffusion Tensor Image"); mimeType.SetCategory(category); mimeType.AddExtension("dti"); return mimeType; } CustomMimeType DiffusionCoreIOMimeTypes::ODF_MIMETYPE() { CustomMimeType mimeType(ODF_MIMETYPE_NAME()); std::string category = "ODF Image"; mimeType.SetComment("Diffusion ODF Image"); mimeType.SetCategory(category); mimeType.AddExtension("odf"); mimeType.AddExtension("qbi"); // legacy support return mimeType; } // Names std::string DiffusionCoreIOMimeTypes::PEAK_MIMETYPE_NAME() { static std::string name = IOMimeTypes::DEFAULT_BASE_NAME() + "_PEAKS"; return name; } std::string DiffusionCoreIOMimeTypes::DWI_NRRD_MIMETYPE_NAME() { static std::string name = IOMimeTypes::DEFAULT_BASE_NAME() + ".dwi"; return name; } std::string DiffusionCoreIOMimeTypes::DWI_NIFTI_MIMETYPE_NAME() { static std::string name = IOMimeTypes::DEFAULT_BASE_NAME() + ".nii.gz"; return name; } std::string DiffusionCoreIOMimeTypes::DWI_FSL_MIMETYPE_NAME() { static std::string name = IOMimeTypes::DEFAULT_BASE_NAME() + ".fslgz"; return name; } std::string DiffusionCoreIOMimeTypes::DWI_DICOM_MIMETYPE_NAME() { static std::string name = IOMimeTypes::DEFAULT_BASE_NAME() + ".IMA"; return name; } std::string DiffusionCoreIOMimeTypes::DTI_MIMETYPE_NAME() { static std::string name = IOMimeTypes::DEFAULT_BASE_NAME() + ".dti"; return name; } std::string DiffusionCoreIOMimeTypes::ODF_MIMETYPE_NAME() { static std::string name = IOMimeTypes::DEFAULT_BASE_NAME() + ".odf"; return name; } std::string DiffusionCoreIOMimeTypes::SH_MIMETYPE_NAME() { static std::string name = IOMimeTypes::DEFAULT_BASE_NAME() + "_SH"; return name; } // Descriptions std::string DiffusionCoreIOMimeTypes::PEAK_MIMETYPE_DESCRIPTION() { static std::string description = "Peak Image"; return description; } std::string DiffusionCoreIOMimeTypes::DWI_NRRD_MIMETYPE_DESCRIPTION() { static std::string description = "Diffusion Weighted Images"; return description; } std::string DiffusionCoreIOMimeTypes::DWI_NIFTI_MIMETYPE_DESCRIPTION() { static std::string description = "Diffusion Weighted Images"; return description; } std::string DiffusionCoreIOMimeTypes::DWI_FSL_MIMETYPE_DESCRIPTION() { static std::string description = "Diffusion Weighted Images"; return description; } std::string DiffusionCoreIOMimeTypes::DWI_DICOM_MIMETYPE_DESCRIPTION() { static std::string description = "Diffusion Weighted Images"; return description; } std::string DiffusionCoreIOMimeTypes::DTI_MIMETYPE_DESCRIPTION() { static std::string description = "Diffusion Tensor Image"; return description; } std::string DiffusionCoreIOMimeTypes::ODF_MIMETYPE_DESCRIPTION() { static std::string description = "ODF Image"; return description; } std::string DiffusionCoreIOMimeTypes::SH_MIMETYPE_DESCRIPTION() { static std::string description = "SH Image"; return description; } } diff --git a/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkPeakImageReader.cpp b/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkPeakImageReader.cpp index 1fd94331b0..e0fe903cef 100644 --- a/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkPeakImageReader.cpp +++ b/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkPeakImageReader.cpp @@ -1,76 +1,83 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkPeakImageReader.h" #include #include #include #include #include #include #include #include #include #include #include namespace mitk { PeakImageReader::PeakImageReader(const PeakImageReader& other) : mitk::AbstractFileReader(other) { } PeakImageReader::PeakImageReader() : mitk::AbstractFileReader( CustomMimeType( mitk::DiffusionCoreIOMimeTypes::PEAK_MIMETYPE() ), mitk::DiffusionCoreIOMimeTypes::PEAK_MIMETYPE_DESCRIPTION() ) { m_ServiceReg = this->RegisterService(); } PeakImageReader::~PeakImageReader() { } std::vector > PeakImageReader::Read() { std::vector > result; + std::string location = GetInputLocation(); + std::string ext = itksys::SystemTools::GetFilenameExtension(location); typedef itk::ImageFileReader FileReaderType; FileReaderType::Pointer reader = FileReaderType::New(); reader->SetFileName(GetInputLocation()); + if (ext==".peak") + { + itk::NrrdImageIO::Pointer io = itk::NrrdImageIO::New(); + reader->SetImageIO(io); + } reader->Update(); Image::Pointer resultImage = dynamic_cast(PeakImage::New().GetPointer()); mitk::CastToMitkImage(reader->GetOutput(), resultImage); resultImage->SetVolume(reader->GetOutput()->GetBufferPointer()); StringProperty::Pointer nameProp; nameProp = StringProperty::New(itksys::SystemTools::GetFilenameWithoutExtension(GetInputLocation())); resultImage->SetProperty("name", nameProp); dynamic_cast(resultImage.GetPointer())->ConstructPolydata(); result.push_back( resultImage.GetPointer() ); return result; } } //namespace MITK mitk::PeakImageReader* mitk::PeakImageReader::Clone() const { return new PeakImageReader(*this); } diff --git a/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkPeakImageSerializer.cpp b/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkPeakImageSerializer.cpp index 87504a1ab5..b2f7168b15 100644 --- a/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkPeakImageSerializer.cpp +++ b/Modules/DiffusionImaging/DiffusionCore/autoload/IO/mitkPeakImageSerializer.cpp @@ -1,80 +1,84 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkPeakImageSerializer.h" #include "mitkPeakImage.h" #include "itkImageFileWriter.h" #include #include - +#include MITK_REGISTER_SERIALIZER(PeakImageSerializer) mitk::PeakImageSerializer::PeakImageSerializer() { } mitk::PeakImageSerializer::~PeakImageSerializer() { } std::string mitk::PeakImageSerializer::Serialize() { const PeakImage* image = dynamic_cast( m_Data.GetPointer() ); if (image == nullptr) { MITK_ERROR << " Object at " << (const void*) this->m_Data << " is not an mitk::PeakImage. Cannot serialize as Nrrd."; return ""; } std::string filename( this->GetUniqueFilenameInWorkingDirectory() ); filename += "_"; filename += m_FilenameHint; - filename += ".nrrd"; + filename += ".peak"; std::string fullname(m_WorkingDirectory); fullname += "/"; fullname += itksys::SystemTools::ConvertToOutputPath(filename.c_str()); try { typedef mitk::ImageToItk< PeakImage::ItkPeakImageType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(image); caster->Update(); mitk::PeakImage::ItkPeakImageType::Pointer itk_image = caster->GetOutput(); itk::ImageFileWriter< PeakImage::ItkPeakImageType >::Pointer writer = itk::ImageFileWriter< PeakImage::ItkPeakImageType >::New(); writer->SetInput(itk_image); writer->SetFileName(fullname); + itk::NrrdImageIO::Pointer io = itk::NrrdImageIO::New(); + io->SetFileType( itk::ImageIOBase::Binary ); + io->UseCompressionOn(); + writer->SetImageIO(io); writer->Update(); } catch (std::exception& e) { MITK_ERROR << " Error serializing object at " << (const void*) this->m_Data << " to " << fullname << ": " << e.what(); return ""; } return filename; } diff --git a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkFitFibersToImageFilter.cpp b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkFitFibersToImageFilter.cpp index 13c67551dc..10c19232b0 100644 --- a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkFitFibersToImageFilter.cpp +++ b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkFitFibersToImageFilter.cpp @@ -1,764 +1,953 @@ #include "itkFitFibersToImageFilter.h" #include namespace itk{ FitFibersToImageFilter::FitFibersToImageFilter() : m_PeakImage(nullptr) + , m_DiffImage(nullptr) + , m_ScalarImage(nullptr) , m_MaskImage(nullptr) , m_FitIndividualFibers(true) , m_GradientTolerance(1e-5) - , m_Lambda(1.0) + , m_Lambda(0.1) , m_MaxIterations(20) , m_FiberSampling(10) , m_Coverage(0) , m_Overshoot(0) , m_RMSE(0.0) - , m_FilterOutliers(true) + , m_FilterOutliers(false) , m_MeanWeight(1.0) , m_MedianWeight(1.0) , m_MinWeight(1.0) , m_MaxWeight(1.0) , m_Verbose(true) , m_DeepCopy(true) , m_ResampleFibers(true) , m_NumUnknowns(0) , m_NumResiduals(0) , m_NumCoveredDirections(0) , m_SignalModel(nullptr) , sz_x(0) , sz_y(0) , sz_z(0) , m_MeanTractDensity(0) , m_MeanSignal(0) , fiber_count(0) - , m_Regularization(VnlCostFunction::REGU::Local_MSE) + , m_Regularization(VnlCostFunction::REGU::VOXEL_VARIANCE) { this->SetNumberOfRequiredOutputs(3); } FitFibersToImageFilter::~FitFibersToImageFilter() { } void FitFibersToImageFilter::CreateDiffSystem() { sz_x = m_DiffImage->GetLargestPossibleRegion().GetSize(0); sz_y = m_DiffImage->GetLargestPossibleRegion().GetSize(1); sz_z = m_DiffImage->GetLargestPossibleRegion().GetSize(2); dim_four_size = m_DiffImage->GetVectorLength(); int num_voxels = sz_x*sz_y*sz_z; float minSpacing = 1; if(m_DiffImage->GetSpacing()[0]GetSpacing()[1] && m_DiffImage->GetSpacing()[0]GetSpacing()[2]) minSpacing = m_DiffImage->GetSpacing()[0]; else if (m_DiffImage->GetSpacing()[1] < m_DiffImage->GetSpacing()[2]) minSpacing = m_DiffImage->GetSpacing()[1]; else minSpacing = m_DiffImage->GetSpacing()[2]; if (m_ResampleFibers) for (unsigned int bundle=0; bundleGetDeepCopy(); m_Tractograms.at(bundle)->ResampleLinear(minSpacing/m_FiberSampling); std::cout.rdbuf (old); } m_NumResiduals = num_voxels * dim_four_size; MITK_INFO << "Num. unknowns: " << m_NumUnknowns; MITK_INFO << "Num. residuals: " << m_NumResiduals; MITK_INFO << "Creating system ..."; A.set_size(m_NumResiduals, m_NumUnknowns); b.set_size(m_NumResiduals); b.fill(0.0); m_MeanTractDensity = 0; m_MeanSignal = 0; m_NumCoveredDirections = 0; fiber_count = 0; vnl_vector voxel_indicator; voxel_indicator.set_size(sz_x*sz_y*sz_z); voxel_indicator.fill(0); m_GroupSizes.clear(); for (unsigned int bundle=0; bundle polydata = m_Tractograms.at(bundle)->GetFiberPolyData(); m_GroupSizes.push_back(m_Tractograms.at(bundle)->GetNumFibers()); for (int i=0; iGetNumFibers(); ++i) { vtkCell* cell = polydata->GetCell(i); int numPoints = cell->GetNumberOfPoints(); vtkPoints* points = cell->GetPoints(); if (numPoints<2) MITK_INFO << "FIBER WITH ONLY ONE POINT ENCOUNTERED!"; for (int j=0; jGetPoint(j); PointType3 p; p[0]=p1[0]; p[1]=p1[1]; p[2]=p1[2]; itk::Index<3> idx3; m_DiffImage->TransformPhysicalPointToIndex(p, idx3); if (!m_DiffImage->GetLargestPossibleRegion().IsInside(idx3) || (m_MaskImage.IsNotNull() && m_MaskImage->GetPixel(idx3)==0)) continue; double* p2 = points->GetPoint(j+1); mitk::DiffusionSignalModel<>::GradientType fiber_dir; fiber_dir[0] = p[0]-p2[0]; fiber_dir[1] = p[1]-p2[1]; fiber_dir[2] = p[2]-p2[2]; fiber_dir.Normalize(); int x = idx3[0]; int y = idx3[1]; int z = idx3[2]; mitk::DiffusionSignalModel<>::PixelType simulated_pixel = m_SignalModel->SimulateMeasurement(fiber_dir); VectorImgType::PixelType measured_pixel = m_DiffImage->GetPixel(idx3); double simulated_mean = 0; double measured_mean = 0; int num_nonzero_g = 0; for (int g=0; gGetGradientDirection(g).GetNorm()GetLargestPossibleRegion().GetSize(0); sz_y = m_PeakImage->GetLargestPossibleRegion().GetSize(1); sz_z = m_PeakImage->GetLargestPossibleRegion().GetSize(2); dim_four_size = m_PeakImage->GetLargestPossibleRegion().GetSize(3)/3 + 1; // +1 for zero - peak int num_voxels = sz_x*sz_y*sz_z; float minSpacing = 1; if(m_PeakImage->GetSpacing()[0]GetSpacing()[1] && m_PeakImage->GetSpacing()[0]GetSpacing()[2]) minSpacing = m_PeakImage->GetSpacing()[0]; else if (m_PeakImage->GetSpacing()[1] < m_PeakImage->GetSpacing()[2]) minSpacing = m_PeakImage->GetSpacing()[1]; else minSpacing = m_PeakImage->GetSpacing()[2]; if (m_ResampleFibers) for (unsigned int bundle=0; bundleGetDeepCopy(); m_Tractograms.at(bundle)->ResampleLinear(minSpacing/m_FiberSampling); std::cout.rdbuf (old); } m_NumResiduals = num_voxels * dim_four_size; MITK_INFO << "Num. unknowns: " << m_NumUnknowns; MITK_INFO << "Num. residuals: " << m_NumResiduals; MITK_INFO << "Creating system ..."; A.set_size(m_NumResiduals, m_NumUnknowns); b.set_size(m_NumResiduals); b.fill(0.0); m_MeanTractDensity = 0; m_MeanSignal = 0; m_NumCoveredDirections = 0; fiber_count = 0; m_GroupSizes.clear(); for (unsigned int bundle=0; bundle polydata = m_Tractograms.at(bundle)->GetFiberPolyData(); m_GroupSizes.push_back(m_Tractograms.at(bundle)->GetNumFibers()); for (int i=0; iGetNumFibers(); ++i) { vtkCell* cell = polydata->GetCell(i); int numPoints = cell->GetNumberOfPoints(); vtkPoints* points = cell->GetPoints(); if (numPoints<2) MITK_INFO << "FIBER WITH ONLY ONE POINT ENCOUNTERED!"; for (int j=0; jGetPoint(j); PointType4 p; p[0]=p1[0]; p[1]=p1[1]; p[2]=p1[2]; p[3]=0; itk::Index<4> idx4; m_PeakImage->TransformPhysicalPointToIndex(p, idx4); itk::Index<3> idx3; idx3[0] = idx4[0]; idx3[1] = idx4[1]; idx3[2] = idx4[2]; if (!m_PeakImage->GetLargestPossibleRegion().IsInside(idx4) || (m_MaskImage.IsNotNull() && m_MaskImage->GetPixel(idx3)==0)) continue; double* p2 = points->GetPoint(j+1); vnl_vector_fixed fiber_dir; fiber_dir[0] = p[0]-p2[0]; fiber_dir[1] = p[1]-p2[1]; fiber_dir[2] = p[2]-p2[2]; fiber_dir.normalize(); double w = 1; int peak_id = dim_four_size-1; vnl_vector_fixed odf_peak = GetClosestPeak(idx4, m_PeakImage, fiber_dir, peak_id, w); float peak_mag = odf_peak.magnitude(); int x = idx4[0]; int y = idx4[1]; int z = idx4[2]; unsigned int linear_index = x + sz_x*y + sz_x*sz_y*z + sz_x*sz_y*sz_z*peak_id; if (b[linear_index] == 0 && peak_idGetLargestPossibleRegion().GetSize(0); + sz_y = m_ScalarImage->GetLargestPossibleRegion().GetSize(1); + sz_z = m_ScalarImage->GetLargestPossibleRegion().GetSize(2); + int num_voxels = sz_x*sz_y*sz_z; + + float minSpacing = 1; + if(m_ScalarImage->GetSpacing()[0]GetSpacing()[1] && m_ScalarImage->GetSpacing()[0]GetSpacing()[2]) + minSpacing = m_ScalarImage->GetSpacing()[0]; + else if (m_ScalarImage->GetSpacing()[1] < m_ScalarImage->GetSpacing()[2]) + minSpacing = m_ScalarImage->GetSpacing()[1]; + else + minSpacing = m_ScalarImage->GetSpacing()[2]; + + if (m_ResampleFibers) + for (unsigned int bundle=0; bundleGetDeepCopy(); + m_Tractograms.at(bundle)->ResampleLinear(minSpacing/m_FiberSampling); + std::cout.rdbuf (old); + } + + m_NumResiduals = num_voxels; + + MITK_INFO << "Num. unknowns: " << m_NumUnknowns; + MITK_INFO << "Num. residuals: " << m_NumResiduals; + MITK_INFO << "Creating system ..."; + + A.set_size(m_NumResiduals, m_NumUnknowns); + b.set_size(m_NumResiduals); b.fill(0.0); + + m_MeanTractDensity = 0; + m_MeanSignal = 0; + int numCoveredVoxels = 0; + fiber_count = 0; + + m_GroupSizes.clear(); + for (unsigned int bundle=0; bundle polydata = m_Tractograms.at(bundle)->GetFiberPolyData(); + + m_GroupSizes.push_back(m_Tractograms.at(bundle)->GetNumFibers()); + for (int i=0; iGetNumFibers(); ++i) + { + vtkCell* cell = polydata->GetCell(i); + int numPoints = cell->GetNumberOfPoints(); + vtkPoints* points = cell->GetPoints(); + + for (int j=0; jGetPoint(j); + PointType3 p; + p[0]=p1[0]; + p[1]=p1[1]; + p[2]=p1[2]; + + itk::Index<3> idx3; + m_ScalarImage->TransformPhysicalPointToIndex(p, idx3); + if (!m_ScalarImage->GetLargestPossibleRegion().IsInside(idx3) || (m_MaskImage.IsNotNull() && m_MaskImage->GetPixel(idx3)==0)) + continue; + + float image_value = m_ScalarImage->GetPixel(idx3); + int x = idx3[0]; + int y = idx3[1]; + int z = idx3[2]; + + unsigned int linear_index = x + sz_x*y + sz_x*sz_y*z; + + if (b[linear_index] == 0) + { + numCoveredVoxels++; + m_MeanSignal += image_value; + } + m_MeanTractDensity += 1; + + if (m_FitIndividualFibers) + { + b[linear_index] = image_value; + A.put(linear_index, fiber_count, A.get(linear_index, fiber_count) + 1); + } + else + { + b[linear_index] = image_value; + A.put(linear_index, bundle, A.get(linear_index, bundle) + 1); + } + } + + ++fiber_count; + } + } + m_MeanTractDensity /= (numCoveredVoxels*fiber_count); + m_MeanSignal /= numCoveredVoxels; + A /= m_MeanTractDensity; + b *= 100.0/m_MeanSignal; // times 100 because we want to avoid too small values for computational reasons + + // NEW FIT +// m_MeanTractDensity /= numCoveredVoxels; +// m_MeanSignal /= numCoveredVoxels; +// b /= m_MeanSignal; +// b *= m_MeanTractDensity; +} + void FitFibersToImageFilter::GenerateData() { m_NumUnknowns = m_Tractograms.size(); if (m_FitIndividualFibers) { m_NumUnknowns = 0; for (unsigned int bundle=0; bundleGetNumFibers(); } else m_FilterOutliers = false; if (m_NumUnknowns<1) { MITK_INFO << "No fibers in tractogram."; return; } fiber_count = 0; sz_x = 0; sz_y = 0; sz_z = 0; m_MeanTractDensity = 0; m_MeanSignal = 0; if (m_PeakImage.IsNotNull()) CreatePeakSystem(); else if (m_DiffImage.IsNotNull()) CreateDiffSystem(); + else if (m_ScalarImage.IsNotNull()) + CreateScalarSystem(); else mitkThrow() << "No input image set!"; double init_lambda = fiber_count; // initialization for lambda estimation itk::TimeProbe clock; clock.Start(); cost = VnlCostFunction(m_NumUnknowns); cost.SetProblem(A, b, init_lambda, m_Regularization); cost.SetGroupSizes(m_GroupSizes); m_Weights.set_size(m_NumUnknowns); m_Weights.fill( 1.0/m_NumUnknowns ); vnl_lbfgsb minimizer(cost); vnl_vector l; l.set_size(m_NumUnknowns); l.fill(0); vnl_vector bound_selection; bound_selection.set_size(m_NumUnknowns); bound_selection.fill(1); minimizer.set_bound_selection(bound_selection); minimizer.set_lower_bound(l); minimizer.set_projected_gradient_tolerance(m_GradientTolerance); MITK_INFO << "Regularization type: " << m_Regularization; if (m_Regularization!=VnlCostFunction::REGU::NONE) // REMOVE FOR NEW FIT AND SET cost.m_Lambda = m_Lambda { MITK_INFO << "Estimating regularization"; minimizer.set_trace(false); minimizer.set_max_function_evals(2); minimizer.minimize(m_Weights); vnl_vector dx; dx.set_size(m_NumUnknowns); dx.fill(0.0); cost.calc_regularization_gradient(m_Weights, dx); double r = dx.magnitude()/m_Weights.magnitude(); // wtf??? cost.m_Lambda *= m_Lambda*55.0/r; MITK_INFO << r << " - " << m_Lambda*55.0/r; if (cost.m_Lambda>10e7) { MITK_INFO << "Regularization estimation failed. Using default value."; cost.m_Lambda = fiber_count; } } MITK_INFO << "Using regularization factor of " << cost.m_Lambda << " (λ: " << m_Lambda << ")"; MITK_INFO << "Fitting fibers"; minimizer.set_trace(m_Verbose); minimizer.set_max_function_evals(m_MaxIterations); minimizer.minimize(m_Weights); std::vector< double > weights; if (m_FilterOutliers) { for (auto w : m_Weights) weights.push_back(w); std::sort(weights.begin(), weights.end()); MITK_INFO << "Setting upper weight bound to " << weights.at(m_NumUnknowns*0.99); vnl_vector u; u.set_size(m_NumUnknowns); u.fill(weights.at(m_NumUnknowns*0.99)); minimizer.set_upper_bound(u); bound_selection.fill(2); minimizer.set_bound_selection(bound_selection); minimizer.minimize(m_Weights); weights.clear(); } for (auto w : m_Weights) weights.push_back(w); std::sort(weights.begin(), weights.end()); m_MeanWeight = m_Weights.mean(); m_MedianWeight = weights.at(m_NumUnknowns*0.5); m_MinWeight = weights.at(0); m_MaxWeight = weights.at(m_NumUnknowns-1); MITK_INFO << "*************************"; MITK_INFO << "Weight statistics"; MITK_INFO << "Sum: " << m_Weights.sum(); MITK_INFO << "Mean: " << m_MeanWeight; MITK_INFO << "1% quantile: " << weights.at(m_NumUnknowns*0.01); MITK_INFO << "5% quantile: " << weights.at(m_NumUnknowns*0.05); MITK_INFO << "25% quantile: " << weights.at(m_NumUnknowns*0.25); MITK_INFO << "Median: " << m_MedianWeight; MITK_INFO << "75% quantile: " << weights.at(m_NumUnknowns*0.75); MITK_INFO << "95% quantile: " << weights.at(m_NumUnknowns*0.95); MITK_INFO << "99% quantile: " << weights.at(m_NumUnknowns*0.99); MITK_INFO << "Min: " << m_MinWeight; MITK_INFO << "Max: " << m_MaxWeight; MITK_INFO << "*************************"; MITK_INFO << "NumEvals: " << minimizer.get_num_evaluations(); MITK_INFO << "NumIterations: " << minimizer.get_num_iterations(); MITK_INFO << "Residual cost: " << minimizer.get_end_error(); m_RMSE = cost.S->get_rms_error(m_Weights); MITK_INFO << "Final RMS: " << m_RMSE; clock.Stop(); int h = clock.GetTotal()/3600; int m = ((int)clock.GetTotal()%3600)/60; int s = (int)clock.GetTotal()%60; MITK_INFO << "Optimization took " << h << "h, " << m << "m and " << s << "s"; MITK_INFO << "Weighting fibers"; m_RmsDiffPerBundle.set_size(m_Tractograms.size()); std::streambuf *old = cout.rdbuf(); // <-- save std::stringstream ss; std::cout.rdbuf (ss.rdbuf()); if (m_FitIndividualFibers) { unsigned int fiber_count = 0; for (unsigned int bundle=0; bundle temp_weights; temp_weights.set_size(m_Weights.size()); temp_weights.copy_in(m_Weights.data_block()); for (int i=0; iGetNumFibers(); i++) { m_Tractograms.at(bundle)->SetFiberWeight(i, m_Weights[fiber_count]); temp_weights[fiber_count] = 0; ++fiber_count; } double d_rms = cost.S->get_rms_error(temp_weights) - m_RMSE; m_RmsDiffPerBundle[bundle] = d_rms; m_Tractograms.at(bundle)->Compress(0.1); m_Tractograms.at(bundle)->ColorFibersByFiberWeights(false, true); } } else { for (unsigned int i=0; i temp_weights; temp_weights.set_size(m_Weights.size()); temp_weights.copy_in(m_Weights.data_block()); temp_weights[i] = 0; double d_rms = cost.S->get_rms_error(temp_weights) - m_RMSE; m_RmsDiffPerBundle[i] = d_rms; m_Tractograms.at(i)->SetFiberWeights(m_Weights[i]); m_Tractograms.at(i)->Compress(0.1); m_Tractograms.at(i)->ColorFibersByFiberWeights(false, true); } } std::cout.rdbuf (old); // transform back A *= m_MeanSignal/100.0; b *= m_MeanSignal/100.0; MITK_INFO << "Generating output images ..."; if (m_PeakImage.IsNotNull()) GenerateOutputPeakImages(); else if (m_DiffImage.IsNotNull()) GenerateOutputDiffImages(); + else if (m_ScalarImage.IsNotNull()) + GenerateOutputScalarImages(); m_Coverage = m_Coverage/m_MeanSignal; m_Overshoot = m_Overshoot/m_MeanSignal; MITK_INFO << std::fixed << "Coverage: " << setprecision(2) << 100.0*m_Coverage << "%"; MITK_INFO << std::fixed << "Overshoot: " << setprecision(2) << 100.0*m_Overshoot << "%"; } void FitFibersToImageFilter::GenerateOutputDiffImages() { VectorImgType::PixelType pix; pix.SetSize(m_DiffImage->GetVectorLength()); pix.Fill(0); itk::ImageDuplicator< VectorImgType >::Pointer duplicator = itk::ImageDuplicator< VectorImgType >::New(); duplicator->SetInputImage(m_DiffImage); duplicator->Update(); m_UnderexplainedImageDiff = duplicator->GetOutput(); m_UnderexplainedImageDiff->FillBuffer(pix); duplicator->SetInputImage(m_UnderexplainedImageDiff); duplicator->Update(); m_OverexplainedImageDiff = duplicator->GetOutput(); m_OverexplainedImageDiff->FillBuffer(pix); duplicator->SetInputImage(m_OverexplainedImageDiff); duplicator->Update(); m_ResidualImageDiff = duplicator->GetOutput(); m_ResidualImageDiff->FillBuffer(pix); duplicator->SetInputImage(m_ResidualImageDiff); duplicator->Update(); m_FittedImageDiff = duplicator->GetOutput(); m_FittedImageDiff->FillBuffer(pix); vnl_vector fitted_b; fitted_b.set_size(b.size()); cost.S->multiply(m_Weights, fitted_b); itk::ImageRegionIterator it1 = itk::ImageRegionIterator(m_DiffImage, m_DiffImage->GetLargestPossibleRegion()); itk::ImageRegionIterator it2 = itk::ImageRegionIterator(m_FittedImageDiff, m_FittedImageDiff->GetLargestPossibleRegion()); itk::ImageRegionIterator it3 = itk::ImageRegionIterator(m_ResidualImageDiff, m_ResidualImageDiff->GetLargestPossibleRegion()); itk::ImageRegionIterator it4 = itk::ImageRegionIterator(m_UnderexplainedImageDiff, m_UnderexplainedImageDiff->GetLargestPossibleRegion()); itk::ImageRegionIterator it5 = itk::ImageRegionIterator(m_OverexplainedImageDiff, m_OverexplainedImageDiff->GetLargestPossibleRegion()); m_MeanSignal = 0; m_Coverage = 0; m_Overshoot = 0; while( !it2.IsAtEnd() ) { itk::Index<3> idx3 = it2.GetIndex(); VectorImgType::PixelType original_pix =it1.Get(); VectorImgType::PixelType fitted_pix =it2.Get(); VectorImgType::PixelType residual_pix =it3.Get(); VectorImgType::PixelType underexplained_pix =it4.Get(); VectorImgType::PixelType overexplained_pix =it5.Get(); int num_nonzero_g = 0; double original_mean = 0; for (int g=0; gGetGradientDirection(g).GetNorm()>=mitk::eps ) { original_mean += original_pix[g]; ++num_nonzero_g; } } original_mean /= num_nonzero_g; for (int g=0; g=0) { underexplained_pix[g] = residual_pix[g]; m_Coverage += fitted_b[linear_index] + original_mean; } m_MeanSignal += b[linear_index] + original_mean; } it2.Set(fitted_pix); it3.Set(residual_pix); it4.Set(underexplained_pix); it5.Set(overexplained_pix); ++it1; ++it2; ++it3; ++it4; ++it5; } } +void FitFibersToImageFilter::GenerateOutputScalarImages() +{ + itk::ImageDuplicator< DoubleImgType >::Pointer duplicator = itk::ImageDuplicator< DoubleImgType >::New(); + duplicator->SetInputImage(m_ScalarImage); + duplicator->Update(); + m_UnderexplainedImageScalar = duplicator->GetOutput(); + m_UnderexplainedImageScalar->FillBuffer(0); + + duplicator->SetInputImage(m_UnderexplainedImageScalar); + duplicator->Update(); + m_OverexplainedImageScalar = duplicator->GetOutput(); + m_OverexplainedImageScalar->FillBuffer(0); + + duplicator->SetInputImage(m_OverexplainedImageScalar); + duplicator->Update(); + m_ResidualImageScalar = duplicator->GetOutput(); + m_ResidualImageScalar->FillBuffer(0); + + duplicator->SetInputImage(m_ResidualImageScalar); + duplicator->Update(); + m_FittedImageScalar = duplicator->GetOutput(); + m_FittedImageScalar->FillBuffer(0); + + vnl_vector fitted_b; fitted_b.set_size(b.size()); + cost.S->multiply(m_Weights, fitted_b); + + itk::ImageRegionIterator it1 = itk::ImageRegionIterator(m_ScalarImage, m_ScalarImage->GetLargestPossibleRegion()); + itk::ImageRegionIterator it2 = itk::ImageRegionIterator(m_FittedImageScalar, m_FittedImageScalar->GetLargestPossibleRegion()); + itk::ImageRegionIterator it3 = itk::ImageRegionIterator(m_ResidualImageScalar, m_ResidualImageScalar->GetLargestPossibleRegion()); + itk::ImageRegionIterator it4 = itk::ImageRegionIterator(m_UnderexplainedImageScalar, m_UnderexplainedImageScalar->GetLargestPossibleRegion()); + itk::ImageRegionIterator it5 = itk::ImageRegionIterator(m_OverexplainedImageScalar, m_OverexplainedImageScalar->GetLargestPossibleRegion()); + + m_MeanSignal = 0; + m_Coverage = 0; + m_Overshoot = 0; + + while( !it2.IsAtEnd() ) + { + itk::Index<3> idx3 = it2.GetIndex(); + DoubleImgType::PixelType original_pix =it1.Get(); + DoubleImgType::PixelType fitted_pix =it2.Get(); + DoubleImgType::PixelType residual_pix =it3.Get(); + DoubleImgType::PixelType underexplained_pix =it4.Get(); + DoubleImgType::PixelType overexplained_pix =it5.Get(); + + unsigned int linear_index = idx3[0] + sz_x*idx3[1] + sz_x*sz_y*idx3[2]; + + fitted_pix = fitted_b[linear_index]; + residual_pix = original_pix - fitted_pix; + + if (residual_pix<0) + { + overexplained_pix = residual_pix; + m_Coverage += b[linear_index]; + m_Overshoot -= residual_pix; + } + else if (residual_pix>=0) + { + underexplained_pix = residual_pix; + m_Coverage += fitted_b[linear_index]; + } + m_MeanSignal += b[linear_index]; + + it2.Set(fitted_pix); + it3.Set(residual_pix); + it4.Set(underexplained_pix); + it5.Set(overexplained_pix); + + ++it1; + ++it2; + ++it3; + ++it4; + ++it5; + } +} + VnlCostFunction::REGU FitFibersToImageFilter::GetRegularization() const { return m_Regularization; } void FitFibersToImageFilter::SetRegularization(const VnlCostFunction::REGU &Regularization) { m_Regularization = Regularization; } void FitFibersToImageFilter::GenerateOutputPeakImages() { itk::ImageDuplicator< PeakImgType >::Pointer duplicator = itk::ImageDuplicator< PeakImgType >::New(); duplicator->SetInputImage(m_PeakImage); duplicator->Update(); m_UnderexplainedImage = duplicator->GetOutput(); m_UnderexplainedImage->FillBuffer(0.0); duplicator->SetInputImage(m_UnderexplainedImage); duplicator->Update(); m_OverexplainedImage = duplicator->GetOutput(); m_OverexplainedImage->FillBuffer(0.0); duplicator->SetInputImage(m_OverexplainedImage); duplicator->Update(); m_ResidualImage = duplicator->GetOutput(); m_ResidualImage->FillBuffer(0.0); duplicator->SetInputImage(m_ResidualImage); duplicator->Update(); m_FittedImage = duplicator->GetOutput(); m_FittedImage->FillBuffer(0.0); vnl_vector fitted_b; fitted_b.set_size(b.size()); cost.S->multiply(m_Weights, fitted_b); for (unsigned int r=0; r idx4; unsigned int linear_index = r; idx4[0] = linear_index % sz_x; linear_index /= sz_x; idx4[1] = linear_index % sz_y; linear_index /= sz_y; idx4[2] = linear_index % sz_z; linear_index /= sz_z; int peak_id = linear_index % dim_four_size; if (peak_id peak_dir; idx4[3] = peak_id*3; peak_dir[0] = m_PeakImage->GetPixel(idx4); idx4[3] += 1; peak_dir[1] = m_PeakImage->GetPixel(idx4); idx4[3] += 1; peak_dir[2] = m_PeakImage->GetPixel(idx4); peak_dir.normalize(); peak_dir *= fitted_b[r]; idx4[3] = peak_id*3; m_FittedImage->SetPixel(idx4, peak_dir[0]); idx4[3] += 1; m_FittedImage->SetPixel(idx4, peak_dir[1]); idx4[3] += 1; m_FittedImage->SetPixel(idx4, peak_dir[2]); } } m_MeanSignal = 0; m_Coverage = 0; m_Overshoot = 0; itk::Index<4> idx4; for (idx4[0]=0; idx4[0] idx3; idx3[0] = idx4[0]; idx3[1] = idx4[1]; idx3[2] = idx4[2]; if (m_MaskImage.IsNotNull() && m_MaskImage->GetPixel(idx3)==0) continue; vnl_vector_fixed peak_dir; vnl_vector_fixed fitted_dir; vnl_vector_fixed overshoot_dir; for (idx4[3]=0; idx4[3]<(itk::IndexValueType)m_PeakImage->GetLargestPossibleRegion().GetSize(3); ++idx4[3]) { peak_dir[idx4[3]%3] = m_PeakImage->GetPixel(idx4); fitted_dir[idx4[3]%3] = m_FittedImage->GetPixel(idx4); m_ResidualImage->SetPixel(idx4, m_PeakImage->GetPixel(idx4) - m_FittedImage->GetPixel(idx4)); if (idx4[3]%3==2) { m_MeanSignal += peak_dir.magnitude(); itk::Index<4> tidx= idx4; if (peak_dir.magnitude()>fitted_dir.magnitude()) { m_Coverage += fitted_dir.magnitude(); m_UnderexplainedImage->SetPixel(tidx, peak_dir[2]-fitted_dir[2]); tidx[3]--; m_UnderexplainedImage->SetPixel(tidx, peak_dir[1]-fitted_dir[1]); tidx[3]--; m_UnderexplainedImage->SetPixel(tidx, peak_dir[0]-fitted_dir[0]); } else { overshoot_dir[0] = fitted_dir[0]-peak_dir[0]; overshoot_dir[1] = fitted_dir[1]-peak_dir[1]; overshoot_dir[2] = fitted_dir[2]-peak_dir[2]; m_Coverage += peak_dir.magnitude(); m_Overshoot += overshoot_dir.magnitude(); m_OverexplainedImage->SetPixel(tidx, overshoot_dir[2]); tidx[3]--; m_OverexplainedImage->SetPixel(tidx, overshoot_dir[1]); tidx[3]--; m_OverexplainedImage->SetPixel(tidx, overshoot_dir[0]); } } } } } vnl_vector_fixed FitFibersToImageFilter::GetClosestPeak(itk::Index<4> idx, PeakImgType::Pointer peak_image , vnl_vector_fixed fiber_dir, int& id, double& w ) { int m_NumDirs = peak_image->GetLargestPossibleRegion().GetSize()[3]/3; vnl_vector_fixed out_dir; out_dir.fill(0); float angle = 0.9; for (int i=0; i dir; idx[3] = i*3; dir[0] = peak_image->GetPixel(idx); idx[3] += 1; dir[1] = peak_image->GetPixel(idx); idx[3] += 1; dir[2] = peak_image->GetPixel(idx); float mag = dir.magnitude(); if (magangle) { angle = fabs(a); w = angle; if (a<0) out_dir = -dir; else out_dir = dir; out_dir *= mag; id = i; } } return out_dir; } std::vector FitFibersToImageFilter::GetTractograms() const { return m_Tractograms; } void FitFibersToImageFilter::SetTractograms(const std::vector &tractograms) { m_Tractograms = tractograms; } void FitFibersToImageFilter::SetSignalModel(mitk::DiffusionSignalModel<> *SignalModel) { m_SignalModel = SignalModel; } } diff --git a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkFitFibersToImageFilter.h b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkFitFibersToImageFilter.h index 40d9fd2e0c..81da7ebb77 100644 --- a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkFitFibersToImageFilter.h +++ b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkFitFibersToImageFilter.h @@ -1,467 +1,483 @@ #ifndef __itkFitFibersToImageFilter_h__ #define __itkFitFibersToImageFilter_h__ // MITK #include #include #include #include #include #include #include #include #include #include #include class VnlCostFunction : public vnl_cost_function { public: enum REGU { MSM, - MSE, - Lasso, - Local_MSE, + VARIANCE, + LASSO, + VOXEL_VARIANCE, GROUP_LASSO, - GROUP_MSE, + GROUP_VARIANCE, NONE }; vnl_sparse_matrix_linear_system< double >* S; vnl_sparse_matrix< double > m_A; vnl_sparse_matrix< double > m_A_Ones; // matrix indicating active weights with 1 vnl_vector< double > m_b; double m_Lambda; // regularization factor vnl_vector row_sums; // number of active weights per row vnl_vector local_weight_means; // mean weight of each row REGU regularization; std::vector group_sizes; void SetProblem(vnl_sparse_matrix< double >& A, vnl_vector& b, double lambda, REGU regu) { S = new vnl_sparse_matrix_linear_system(A, b); m_A = A; m_b = b; m_Lambda = lambda; m_A_Ones.set_size(m_A.rows(), m_A.cols()); m_A.reset(); while (m_A.next()) m_A_Ones.put(m_A.getrow(), m_A.getcolumn(), 1); unsigned int N = m_b.size(); vnl_vector ones; ones.set_size(dim); ones.fill(1.0); row_sums.set_size(N); m_A_Ones.mult(ones, row_sums); local_weight_means.set_size(N); regularization = regu; } void SetGroupSizes(std::vector sizes) { unsigned int sum = 0; for (auto s : sizes) sum += s; if (sum!=m_A.cols()) { MITK_INFO << "Group sizes do not match number of unknowns (" << sum << " vs. " << m_A.cols() << ")"; return; } group_sizes = sizes; } VnlCostFunction(const int NumVars=0) : vnl_cost_function(NumVars) { } - // Regularization: mean squared magnitude of weight vectors (small weights) L2 + // Regularization: mean squared magnitude of weight vectors (small weights) void regu_MSM(vnl_vector const &x, double& cost) { cost += 10000.0*m_Lambda*x.squared_magnitude()/dim; } // Regularization: mean squared deaviation of weights from mean weight (enforce uniform weights) - void regu_MSE(vnl_vector const &x, double& cost) + void regu_Variance(vnl_vector const &x, double& cost) { double mean = x.mean(); vnl_vector tx = x-mean; cost += 10000.0*m_Lambda*tx.squared_magnitude()/dim; } // Regularization: mean absolute magnitude of weight vectors (small weights) L1 void regu_Lasso(vnl_vector const &x, double& cost) { cost += 10000.0*m_Lambda*x.one_norm()/dim; } // Regularization: mean squared deaviation of weights from bundle mean weight (enforce uniform weights PER BUNDLE) - void regu_GroupMSE(vnl_vector const &x, double& cost) + void regu_GroupVariance(vnl_vector const &x, double& cost) { vnl_vector tx(x); unsigned int offset = 0; for (auto g : group_sizes) { double group_mean = 0; for (unsigned int i=0; i const &x, double& cost) + void regu_VoxelVariance(vnl_vector const &x, double& cost) { m_A_Ones.mult(x, local_weight_means); local_weight_means = element_quotient(local_weight_means, row_sums); m_A_Ones.reset(); double regu = 0; while (m_A_Ones.next()) { double d = 0; if (x[m_A_Ones.getcolumn()]>local_weight_means[m_A_Ones.getrow()]) d = std::exp(x[m_A_Ones.getcolumn()]) - std::exp(local_weight_means[m_A_Ones.getrow()]); else d = x[m_A_Ones.getcolumn()] - local_weight_means[m_A_Ones.getrow()]; regu += d*d; } cost += m_Lambda*regu/dim; } // Regularization: group Lasso: sum_g(lambda_g * ||x_g||_2) void regu_GroupLasso(vnl_vector const &x, double& cost) { unsigned int offset = 0; for (auto g : group_sizes) { double group_cost = 0; for (unsigned int i=0; i const &x, vnl_vector &dx) { dx += 10000.0*m_Lambda*2.0*x/dim; } - void grad_regu_MSE(vnl_vector const &x, vnl_vector &dx) + void grad_regu_Variance(vnl_vector const &x, vnl_vector &dx) { double mean = x.mean(); vnl_vector tx = x-mean; // difference to mean dx += 10000.0*tx*(2.0-2.0/dim)/dim; } void grad_regu_Lasso(vnl_vector const &x, vnl_vector &dx) { for (int i=0; i0) dx[i] += 10000.0*m_Lambda/dim; } - void grad_regu_GroupMSE(vnl_vector const &x, vnl_vector &dx) + void grad_regu_GroupVariance(vnl_vector const &x, vnl_vector &dx) { vnl_vector tx(x); unsigned int offset = 0; for (auto g : group_sizes) { double group_mean = 0; for (unsigned int i=0; i const &x, vnl_vector &dx) + void grad_regu_VoxelVariance(vnl_vector const &x, vnl_vector &dx) { m_A_Ones.mult(x, local_weight_means); local_weight_means = element_quotient(local_weight_means, row_sums); vnl_vector exp_x = x.apply(std::exp); vnl_vector exp_means = local_weight_means.apply(std::exp); vnl_vector tdx(dim, 0); m_A_Ones.reset(); while (m_A_Ones.next()) { int c = m_A_Ones.getcolumn(); int r = m_A_Ones.getrow(); if (x[c]>local_weight_means[r]) tdx[c] += exp_x[c] * ( exp_x[c] - exp_means[r] ); else tdx[c] += x[c] - local_weight_means[r]; } dx += tdx*2.0*m_Lambda/dim; } void grad_regu_GroupLasso(vnl_vector const &x, vnl_vector &dx) { unsigned int offset = 0; for (auto g : group_sizes) { double group_lambda = m_Lambda*std::sqrt(g)/dim; double group_l2 = 0; for (unsigned int i=0; i0.0) { for (unsigned int i=0; i const &x, double& cost) { - if (regularization==Local_MSE) - regu_localMSE(x, cost); - else if (regularization==MSE) - regu_MSE(x, cost); + if (regularization==VOXEL_VARIANCE) + regu_VoxelVariance(x, cost); + else if (regularization==VARIANCE) + regu_Variance(x, cost); else if (regularization==MSM) regu_MSM(x, cost); - else if (regularization==Lasso) + else if (regularization==LASSO) regu_Lasso(x, cost); else if (regularization==GROUP_LASSO) regu_GroupLasso(x, cost); - else if (regularization==GROUP_MSE) - regu_GroupMSE(x, cost); + else if (regularization==GROUP_VARIANCE) + regu_GroupVariance(x, cost); } void calc_regularization_gradient(vnl_vector const &x, vnl_vector &dx) { - if (regularization==Local_MSE) - grad_regu_localMSE(x,dx); - else if (regularization==MSE) - grad_regu_MSE(x,dx); + if (regularization==VOXEL_VARIANCE) + grad_regu_VoxelVariance(x,dx); + else if (regularization==VARIANCE) + grad_regu_Variance(x,dx); else if (regularization==MSM) grad_regu_MSM(x,dx); - else if (regularization==Lasso) + else if (regularization==LASSO) grad_regu_Lasso(x,dx); else if (regularization==GROUP_LASSO) grad_regu_GroupLasso(x, dx); - else if (regularization==GROUP_MSE) - grad_regu_GroupMSE(x, dx); + else if (regularization==GROUP_VARIANCE) + grad_regu_GroupVariance(x, dx); } // cost function double f(vnl_vector const &x) { // RMS error unsigned int N = m_b.size(); vnl_vector d; d.set_size(N); S->multiply(x,d); double cost = (d - m_b).squared_magnitude()/N; // regularize calc_regularization(x, cost); return cost; } // gradient of cost function void gradf(vnl_vector const &x, vnl_vector &dx) { dx.fill(0.0); unsigned int N = m_b.size(); // calculate output difference d vnl_vector d; d.set_size(N); S->multiply(x,d); d -= m_b; // (f(u(x)))' = f'(u(x)) * u'(x) // d/dx_j = 1/N * Sum_i A_i,j * 2*(A_i,j * x_j - b_i) S->transpose_multiply(d, dx); dx *= 2.0/N; calc_regularization_gradient(x,dx); } }; namespace itk{ /** * \brief Fits the tractogram to the input image by assigning a weight to each fiber (similar to https://doi.org/10.1016/j.neuroimage.2015.06.092). */ class FitFibersToImageFilter : public ImageSource< mitk::PeakImage::ItkPeakImageType > { public: typedef FitFibersToImageFilter Self; typedef ProcessObject Superclass; typedef SmartPointer< Self > Pointer; typedef SmartPointer< const Self > ConstPointer; typedef itk::Point PointType3; typedef itk::Point PointType4; typedef mitk::DiffusionPropertyHelper::ImageType VectorImgType; typedef mitk::PeakImage::ItkPeakImageType PeakImgType; typedef itk::Image UcharImgType; + typedef itk::Image DoubleImgType; itkFactorylessNewMacro(Self) itkCloneMacro(Self) itkTypeMacro( FitFibersToImageFilter, ImageSource ) + itkSetMacro( ScalarImage, DoubleImgType::Pointer) + itkGetMacro( ScalarImage, DoubleImgType::Pointer) itkSetMacro( PeakImage, PeakImgType::Pointer) itkGetMacro( PeakImage, PeakImgType::Pointer) itkSetMacro( DiffImage, VectorImgType::Pointer) itkGetMacro( DiffImage, VectorImgType::Pointer) itkSetMacro( MaskImage, UcharImgType::Pointer) itkGetMacro( MaskImage, UcharImgType::Pointer) itkSetMacro( FitIndividualFibers, bool) itkGetMacro( FitIndividualFibers, bool) itkSetMacro( GradientTolerance, double) itkGetMacro( GradientTolerance, double) itkSetMacro( Lambda, double) itkGetMacro( Lambda, double) itkSetMacro( MaxIterations, int) itkGetMacro( MaxIterations, int) itkSetMacro( FiberSampling, float) itkGetMacro( FiberSampling, float) itkSetMacro( FilterOutliers, bool) itkGetMacro( FilterOutliers, bool) itkSetMacro( Verbose, bool) itkGetMacro( Verbose, bool) itkSetMacro( DeepCopy, bool) itkGetMacro( DeepCopy, bool) itkSetMacro( ResampleFibers, bool) itkGetMacro( ResampleFibers, bool) itkGetMacro( Weights, vnl_vector) itkGetMacro( RmsDiffPerBundle, vnl_vector) itkGetMacro( FittedImage, PeakImgType::Pointer) itkGetMacro( ResidualImage, PeakImgType::Pointer) itkGetMacro( OverexplainedImage, PeakImgType::Pointer) itkGetMacro( UnderexplainedImage, PeakImgType::Pointer) itkGetMacro( FittedImageDiff, VectorImgType::Pointer) itkGetMacro( ResidualImageDiff, VectorImgType::Pointer) itkGetMacro( OverexplainedImageDiff, VectorImgType::Pointer) itkGetMacro( UnderexplainedImageDiff, VectorImgType::Pointer) + itkGetMacro( FittedImageScalar, DoubleImgType::Pointer) + itkGetMacro( ResidualImageScalar, DoubleImgType::Pointer) + itkGetMacro( OverexplainedImageScalar, DoubleImgType::Pointer) + itkGetMacro( UnderexplainedImageScalar, DoubleImgType::Pointer) + itkGetMacro( Coverage, double) itkGetMacro( Overshoot, double) itkGetMacro( RMSE, double) itkGetMacro( MeanWeight, double) itkGetMacro( MedianWeight, double) itkGetMacro( MinWeight, double) itkGetMacro( MaxWeight, double) itkGetMacro( NumUnknowns, unsigned int) itkGetMacro( NumResiduals, unsigned int) itkGetMacro( NumCoveredDirections, unsigned int) void SetTractograms(const std::vector &tractograms); void GenerateData() override; std::vector GetTractograms() const; void SetSignalModel(mitk::DiffusionSignalModel<> *SignalModel); VnlCostFunction::REGU GetRegularization() const; void SetRegularization(const VnlCostFunction::REGU &GetRegularization); protected: FitFibersToImageFilter(); virtual ~FitFibersToImageFilter(); vnl_vector_fixed GetClosestPeak(itk::Index<4> idx, PeakImgType::Pointer m_PeakImage , vnl_vector_fixed fiber_dir, int& id, double& w ); void CreatePeakSystem(); void CreateDiffSystem(); + void CreateScalarSystem(); void GenerateOutputPeakImages(); void GenerateOutputDiffImages(); + void GenerateOutputScalarImages(); std::vector< mitk::FiberBundle::Pointer > m_Tractograms; PeakImgType::Pointer m_PeakImage; VectorImgType::Pointer m_DiffImage; + DoubleImgType::Pointer m_ScalarImage; UcharImgType::Pointer m_MaskImage; bool m_FitIndividualFibers; double m_GradientTolerance; double m_Lambda; int m_MaxIterations; float m_FiberSampling; double m_Coverage; double m_Overshoot; double m_RMSE; bool m_FilterOutliers; double m_MeanWeight; double m_MedianWeight; double m_MinWeight; double m_MaxWeight; bool m_Verbose; bool m_DeepCopy; bool m_ResampleFibers; unsigned int m_NumUnknowns; unsigned int m_NumResiduals; unsigned int m_NumCoveredDirections; // output vnl_vector m_RmsDiffPerBundle; vnl_vector m_Weights; PeakImgType::Pointer m_UnderexplainedImage; PeakImgType::Pointer m_OverexplainedImage; PeakImgType::Pointer m_ResidualImage; PeakImgType::Pointer m_FittedImage; VectorImgType::Pointer m_UnderexplainedImageDiff; VectorImgType::Pointer m_OverexplainedImageDiff; VectorImgType::Pointer m_ResidualImageDiff; VectorImgType::Pointer m_FittedImageDiff; + DoubleImgType::Pointer m_UnderexplainedImageScalar; + DoubleImgType::Pointer m_OverexplainedImageScalar; + DoubleImgType::Pointer m_ResidualImageScalar; + DoubleImgType::Pointer m_FittedImageScalar; + mitk::DiffusionSignalModel<>* m_SignalModel; vnl_sparse_matrix A; vnl_vector b; VnlCostFunction cost; int sz_x; int sz_y; int sz_z; int dim_four_size; double m_MeanTractDensity; double m_MeanSignal; unsigned int fiber_count; VnlCostFunction::REGU m_Regularization; std::vector m_GroupSizes; }; } #ifndef ITK_MANUAL_INSTANTIATION #include "itkFitFibersToImageFilter.cpp" #endif #endif // __itkFitFibersToImageFilter_h__ diff --git a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp index 8b5dbf5ee1..4d3adb37d6 100644 --- a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp +++ b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp @@ -1,1023 +1,1024 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include #include "itkStreamlineTrackingFilter.h" #include #include #include #include #include "itkPointShell.h" #include #include #include #include #include #include #include #include namespace itk { StreamlineTrackingFilter ::StreamlineTrackingFilter() : m_PauseTracking(false) , m_AbortTracking(false) , m_BuildFibersFinished(false) , m_BuildFibersReady(0) , m_FiberPolyData(nullptr) , m_Points(nullptr) , m_Cells(nullptr) , m_StoppingRegions(nullptr) , m_TargetRegions(nullptr) , m_SeedImage(nullptr) , m_MaskImage(nullptr) , m_ExclusionRegions(nullptr) , m_OutputProbabilityMap(nullptr) , m_MinVoxelSize(-1) , m_AngularThresholdDeg(-1) , m_StepSizeVox(-1) , m_SamplingDistanceVox(-1) , m_AngularThreshold(-1) , m_StepSize(0) , m_MaxLength(10000) , m_MinTractLength(20.0) , m_MaxTractLength(400.0) , m_SeedsPerVoxel(1) , m_AvoidStop(true) , m_RandomSampling(false) , m_SamplingDistance(-1) , m_DeflectionMod(1.0) , m_OnlyForwardSamples(true) , m_UseStopVotes(true) , m_NumberOfSamples(30) , m_NumPreviousDirections(1) , m_MaxNumTracts(-1) , m_Verbose(true) , m_LoopCheck(-1) , m_DemoMode(false) , m_Random(true) , m_UseOutputProbabilityMap(false) , m_CurrentTracts(0) , m_Progress(0) , m_StopTracking(false) , m_InterpolateMasks(true) , m_TrialsPerSeed(10) , m_EndpointConstraint(EndpointConstraints::NONE) , m_IntroduceDirectionsFromPrior(true) , m_TrackingPriorAsMask(true) , m_TrackingPriorWeight(1.0) , m_TrackingPriorHandler(nullptr) { this->SetNumberOfRequiredInputs(0); } std::string StreamlineTrackingFilter::GetStatusText() { std::string status = "Seedpoints processed: " + boost::lexical_cast(m_Progress) + "/" + boost::lexical_cast(m_SeedPoints.size()); if (m_SeedPoints.size()>0) status += " (" + boost::lexical_cast(100*m_Progress/m_SeedPoints.size()) + "%)"; if (m_MaxNumTracts>0) status += "\nFibers accepted: " + boost::lexical_cast(m_CurrentTracts) + "/" + boost::lexical_cast(m_MaxNumTracts); else status += "\nFibers accepted: " + boost::lexical_cast(m_CurrentTracts); return status; } void StreamlineTrackingFilter::BeforeTracking() { m_StopTracking = false; m_TrackingHandler->SetRandom(m_Random); m_TrackingHandler->InitForTracking(); m_FiberPolyData = PolyDataType::New(); m_Points = vtkSmartPointer< vtkPoints >::New(); m_Cells = vtkSmartPointer< vtkCellArray >::New(); itk::Vector< double, 3 > imageSpacing = m_TrackingHandler->GetSpacing(); if(imageSpacing[0]SetAngularThreshold(m_AngularThreshold); if (m_TrackingPriorHandler!=nullptr) { m_TrackingPriorHandler->SetRandom(m_Random); m_TrackingPriorHandler->InitForTracking(); m_TrackingPriorHandler->SetAngularThreshold(m_AngularThreshold); } if (m_SamplingDistanceVoxGetNumberOfThreads(); i++) { PolyDataType poly = PolyDataType::New(); m_PolyDataContainer.push_back(poly); } if (m_UseOutputProbabilityMap) { m_OutputProbabilityMap = ItkDoubleImgType::New(); m_OutputProbabilityMap->SetSpacing(imageSpacing); m_OutputProbabilityMap->SetOrigin(m_TrackingHandler->GetOrigin()); m_OutputProbabilityMap->SetDirection(m_TrackingHandler->GetDirection()); m_OutputProbabilityMap->SetRegions(m_TrackingHandler->GetLargestPossibleRegion()); m_OutputProbabilityMap->Allocate(); m_OutputProbabilityMap->FillBuffer(0); } m_MaskInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); m_StopInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); m_SeedInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); m_TargetInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); m_ExclusionInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); if (m_StoppingRegions.IsNull()) { m_StoppingRegions = ItkFloatImgType::New(); m_StoppingRegions->SetSpacing( imageSpacing ); m_StoppingRegions->SetOrigin( m_TrackingHandler->GetOrigin() ); m_StoppingRegions->SetDirection( m_TrackingHandler->GetDirection() ); m_StoppingRegions->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_StoppingRegions->Allocate(); m_StoppingRegions->FillBuffer(0); } else std::cout << "StreamlineTracking - Using stopping region image" << std::endl; m_StopInterpolator->SetInputImage(m_StoppingRegions); if (m_ExclusionRegions.IsNotNull()) { std::cout << "StreamlineTracking - Using exclusion region image" << std::endl; m_ExclusionInterpolator->SetInputImage(m_ExclusionRegions); } if (m_TargetRegions.IsNull()) { m_TargetImageSet = false; m_TargetRegions = ItkFloatImgType::New(); m_TargetRegions->SetSpacing( imageSpacing ); m_TargetRegions->SetOrigin( m_TrackingHandler->GetOrigin() ); m_TargetRegions->SetDirection( m_TrackingHandler->GetDirection() ); m_TargetRegions->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_TargetRegions->Allocate(); m_TargetRegions->FillBuffer(1); } else { m_TargetImageSet = true; m_TargetInterpolator->SetInputImage(m_TargetRegions); std::cout << "StreamlineTracking - Using target region image" << std::endl; } if (m_SeedImage.IsNull()) { m_SeedImageSet = false; m_SeedImage = ItkFloatImgType::New(); m_SeedImage->SetSpacing( imageSpacing ); m_SeedImage->SetOrigin( m_TrackingHandler->GetOrigin() ); m_SeedImage->SetDirection( m_TrackingHandler->GetDirection() ); m_SeedImage->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_SeedImage->Allocate(); m_SeedImage->FillBuffer(1); } else { m_SeedImageSet = true; std::cout << "StreamlineTracking - Using seed image" << std::endl; } m_SeedInterpolator->SetInputImage(m_SeedImage); if (m_MaskImage.IsNull()) { // initialize mask image m_MaskImage = ItkFloatImgType::New(); m_MaskImage->SetSpacing( imageSpacing ); m_MaskImage->SetOrigin( m_TrackingHandler->GetOrigin() ); m_MaskImage->SetDirection( m_TrackingHandler->GetDirection() ); m_MaskImage->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_MaskImage->Allocate(); m_MaskImage->FillBuffer(1); } else std::cout << "StreamlineTracking - Using mask image" << std::endl; m_MaskInterpolator->SetInputImage(m_MaskImage); // Autosettings for endpoint constraints if (m_EndpointConstraint==EndpointConstraints::NONE && m_TargetImageSet && m_SeedImageSet) { MITK_INFO << "No endpoint constraint chosen but seed and target image set --> setting constraint to EPS_IN_SEED_AND_TARGET"; m_EndpointConstraint = EndpointConstraints::EPS_IN_SEED_AND_TARGET; } else if (m_EndpointConstraint==EndpointConstraints::NONE && m_TargetImageSet) { MITK_INFO << "No endpoint constraint chosen but target image set --> setting constraint to EPS_IN_TARGET"; m_EndpointConstraint = EndpointConstraints::EPS_IN_TARGET; } // Check if endpoint constraints are valid FiberType test_fib; itk::Point p; p.Fill(0); test_fib.push_back(p); test_fib.push_back(p); IsValidFiber(&test_fib); if (m_SeedPoints.empty()) GetSeedPointsFromSeedImage(); m_BuildFibersReady = 0; m_BuildFibersFinished = false; m_Tractogram.clear(); m_SamplingPointset = mitk::PointSet::New(); m_AlternativePointset = mitk::PointSet::New(); m_StopVotePointset = mitk::PointSet::New(); m_StartTime = std::chrono::system_clock::now(); if (m_DemoMode) omp_set_num_threads(1); if (m_TrackingHandler->GetMode()==mitk::TrackingDataHandler::MODE::DETERMINISTIC) std::cout << "StreamlineTracking - Mode: deterministic" << std::endl; else if(m_TrackingHandler->GetMode()==mitk::TrackingDataHandler::MODE::PROBABILISTIC) { std::cout << "StreamlineTracking - Mode: probabilistic" << std::endl; std::cout << "StreamlineTracking - Trials per seed: " << m_TrialsPerSeed << std::endl; } else std::cout << "StreamlineTracking - Mode: ???" << std::endl; if (m_EndpointConstraint==EndpointConstraints::NONE) std::cout << "StreamlineTracking - Endpoint constraint: NONE" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: EPS_IN_TARGET" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_TARGET_LABELDIFF) std::cout << "StreamlineTracking - Endpoint constraint: EPS_IN_TARGET_LABELDIFF" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_SEED_AND_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: EPS_IN_SEED_AND_TARGET" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::MIN_ONE_EP_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: MIN_ONE_EP_IN_TARGET" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::ONE_EP_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: ONE_EP_IN_TARGET" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::NO_EP_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: NO_EP_IN_TARGET" << std::endl; std::cout << "StreamlineTracking - Angular threshold: " << m_AngularThreshold << " (" << 180*std::acos( m_AngularThreshold )/itk::Math::pi << "°)" << std::endl; std::cout << "StreamlineTracking - Stepsize: " << m_StepSize << "mm (" << m_StepSize/m_MinVoxelSize << "*vox)" << std::endl; std::cout << "StreamlineTracking - Seeds per voxel: " << m_SeedsPerVoxel << std::endl; std::cout << "StreamlineTracking - Max. tract length: " << m_MaxTractLength << "mm" << std::endl; std::cout << "StreamlineTracking - Min. tract length: " << m_MinTractLength << "mm" << std::endl; std::cout << "StreamlineTracking - Max. num. tracts: " << m_MaxNumTracts << std::endl; std::cout << "StreamlineTracking - Loop check: " << m_LoopCheck << "°" << std::endl; std::cout << "StreamlineTracking - Num. neighborhood samples: " << m_NumberOfSamples << std::endl; std::cout << "StreamlineTracking - Max. sampling distance: " << m_SamplingDistance << "mm (" << m_SamplingDistance/m_MinVoxelSize << "*vox)" << std::endl; std::cout << "StreamlineTracking - Deflection modifier: " << m_DeflectionMod << std::endl; std::cout << "StreamlineTracking - Use stop votes: " << m_UseStopVotes << std::endl; std::cout << "StreamlineTracking - Only frontal samples: " << m_OnlyForwardSamples << std::endl; if (m_TrackingPriorHandler!=nullptr) - std::cout << "StreamlineTracking - Using directional prior for tractography" << std::endl; + std::cout << "StreamlineTracking - Using directional prior for tractography (w=" << m_TrackingPriorWeight << ")" << std::endl; if (m_DemoMode) { std::cout << "StreamlineTracking - Running in demo mode"; std::cout << "StreamlineTracking - Starting streamline tracking using 1 thread" << std::endl; } else std::cout << "StreamlineTracking - Starting streamline tracking using " << omp_get_max_threads() << " threads" << std::endl; } void StreamlineTrackingFilter::CalculateNewPosition(itk::Point& pos, vnl_vector_fixed& dir) { pos[0] += dir[0]*m_StepSize; pos[1] += dir[1]*m_StepSize; pos[2] += dir[2]*m_StepSize; } std::vector< vnl_vector_fixed > StreamlineTrackingFilter::CreateDirections(int NPoints) { std::vector< vnl_vector_fixed > pointshell; if (NPoints<2) return pointshell; std::vector< float > theta; theta.resize(NPoints); std::vector< float > phi; phi.resize(NPoints); float C = sqrt(4*itk::Math::pi); phi[0] = 0.0; phi[NPoints-1] = 0.0; for(int i=0; i0 && i d; d[0] = cos(theta[i]) * cos(phi[i]); d[1] = cos(theta[i]) * sin(phi[i]); d[2] = sin(theta[i]); pointshell.push_back(d); } return pointshell; } vnl_vector_fixed StreamlineTrackingFilter::GetNewDirection(const itk::Point &pos, std::deque >& olddirs, itk::Index<3> &oldIndex) { if (m_DemoMode) { m_SamplingPointset->Clear(); m_AlternativePointset->Clear(); m_StopVotePointset->Clear(); } vnl_vector_fixed direction; direction.fill(0); if (mitk::imv::IsInsideMask(pos, m_InterpolateMasks, m_MaskInterpolator) && !mitk::imv::IsInsideMask(pos, m_InterpolateMasks, m_StopInterpolator)) direction = m_TrackingHandler->ProposeDirection(pos, olddirs, oldIndex); // get direction proposal at current streamline position else return direction; int stop_votes = 0; int possible_stop_votes = 0; if (!olddirs.empty()) { vnl_vector_fixed olddir = olddirs.back(); std::vector< vnl_vector_fixed > probeVecs = CreateDirections(m_NumberOfSamples); itk::Point sample_pos; int alternatives = 1; for (unsigned int i=0; i d; bool is_stop_voter = false; if (m_Random && m_RandomSampling) { d[0] = m_TrackingHandler->GetRandDouble(-0.5, 0.5); d[1] = m_TrackingHandler->GetRandDouble(-0.5, 0.5); d[2] = m_TrackingHandler->GetRandDouble(-0.5, 0.5); d.normalize(); d *= m_TrackingHandler->GetRandDouble(0,m_SamplingDistance); } else { d = probeVecs.at(i); float dot = dot_product(d, olddir); if (m_UseStopVotes && dot>0.7) { is_stop_voter = true; possible_stop_votes++; } else if (m_OnlyForwardSamples && dot<0) continue; d *= m_SamplingDistance; } sample_pos[0] = pos[0] + d[0]; sample_pos[1] = pos[1] + d[1]; sample_pos[2] = pos[2] + d[2]; vnl_vector_fixed tempDir; tempDir.fill(0.0); if (mitk::imv::IsInsideMask(sample_pos, m_InterpolateMasks, m_MaskInterpolator)) tempDir = m_TrackingHandler->ProposeDirection(sample_pos, olddirs, oldIndex); // sample neighborhood if (tempDir.magnitude()>mitk::eps) { direction += tempDir; if(m_DemoMode) m_SamplingPointset->InsertPoint(i, sample_pos); } else if (m_AvoidStop && olddir.magnitude()>0.5) // out of white matter { if (is_stop_voter) stop_votes++; if (m_DemoMode) m_StopVotePointset->InsertPoint(i, sample_pos); float dot = dot_product(d, olddir); if (dot >= 0.0) // in front of plane defined by pos and olddir d = -d + 2*dot*olddir; // reflect else d = -d; // invert // look a bit further into the other direction sample_pos[0] = pos[0] + d[0]; sample_pos[1] = pos[1] + d[1]; sample_pos[2] = pos[2] + d[2]; alternatives++; vnl_vector_fixed tempDir; tempDir.fill(0.0); if (mitk::imv::IsInsideMask(sample_pos, m_InterpolateMasks, m_MaskInterpolator)) tempDir = m_TrackingHandler->ProposeDirection(sample_pos, olddirs, oldIndex); // sample neighborhood if (tempDir.magnitude()>mitk::eps) // are we back in the white matter? { direction += d * m_DeflectionMod; // go into the direction of the white matter direction += tempDir; // go into the direction of the white matter direction at this location if(m_DemoMode) m_AlternativePointset->InsertPoint(alternatives, sample_pos); } else { if (m_DemoMode) m_StopVotePointset->InsertPoint(i, sample_pos); } } else { if (m_DemoMode) m_StopVotePointset->InsertPoint(i, sample_pos); if (is_stop_voter) stop_votes++; } } } bool valid = false; if (direction.magnitude()>0.001 && (possible_stop_votes==0 || (float)stop_votes/possible_stop_votes<0.5) ) { direction.normalize(); valid = true; } else direction.fill(0); if (m_TrackingPriorHandler!=nullptr && (m_IntroduceDirectionsFromPrior || valid)) { vnl_vector_fixed prior = m_TrackingPriorHandler->ProposeDirection(pos, olddirs, oldIndex); if (prior.magnitude()>0.001) { prior.normalize(); if (dot_product(prior,direction)<0) prior *= -1; direction = (1.0f-m_TrackingPriorWeight) * direction + m_TrackingPriorWeight * prior; direction.normalize(); } else if (m_TrackingPriorAsMask) direction.fill(0.0); } return direction; } float StreamlineTrackingFilter::FollowStreamline(itk::Point pos, vnl_vector_fixed dir, FiberType* fib, DirectionContainer* container, float tractLength, bool front, bool &exclude) { vnl_vector_fixed zero_dir; zero_dir.fill(0.0); std::deque< vnl_vector_fixed > last_dirs; for (unsigned int i=0; i oldIndex; m_TrackingHandler->WorldToIndex(pos, oldIndex); // get new position CalculateNewPosition(pos, dir); if (m_ExclusionRegions.IsNotNull() && mitk::imv::IsInsideMask(pos, m_InterpolateMasks, m_ExclusionInterpolator)) { exclude = true; return tractLength; } if (m_AbortTracking) return tractLength; // if yes, add new point to streamline dir.normalize(); if (front) { fib->push_front(pos); container->push_front(dir); } else { fib->push_back(pos); container->push_back(dir); } tractLength += m_StepSize; if (m_LoopCheck>=0 && CheckCurvature(container, front)>m_LoopCheck) return tractLength; if (tractLength>m_MaxTractLength) return tractLength; if (m_DemoMode && !m_UseOutputProbabilityMap) // CHECK: warum sind die samplingpunkte der streamline in der visualisierung immer einen schritt voras? { #pragma omp critical { m_BuildFibersReady++; m_Tractogram.push_back(*fib); BuildFibers(true); m_Stop = true; while (m_Stop){ } } } last_dirs.push_back(dir); if (last_dirs.size()>m_NumPreviousDirections) last_dirs.pop_front(); dir = GetNewDirection(pos, last_dirs, oldIndex); while (m_PauseTracking){} if (dir.magnitude()<0.0001) return tractLength; } return tractLength; } float StreamlineTrackingFilter::CheckCurvature(DirectionContainer* fib, bool front) { if (fib->size()<8) return 0; float m_Distance = std::max(m_MinVoxelSize*4, m_StepSize*8); float dist = 0; std::vector< vnl_vector_fixed< float, 3 > > vectors; vnl_vector_fixed< float, 3 > meanV; meanV.fill(0); float dev = 0; if (front) { int c = 0; while(distsize()-1) { dist += m_StepSize; vnl_vector_fixed< float, 3 > v = fib->at(c); + if (dot_product(v,meanV)<0) + v = -v; vectors.push_back(v); meanV += v; c++; } } else { int c = fib->size()-1; while(dist=0) { dist += m_StepSize; vnl_vector_fixed< float, 3 > v = fib->at(c); + if (dot_product(v,meanV)<0) + v = -v; vectors.push_back(v); meanV += v; c--; } } meanV.normalize(); for (unsigned int c=0; c1.0) angle = 1.0; dev += acos(angle)*180/itk::Math::pi; } if (vectors.size()>0) dev /= vectors.size(); return dev; } void StreamlineTrackingFilter::SetTrackingPriorHandler(mitk::TrackingDataHandler *TrackingPriorHandler) { m_TrackingPriorHandler = TrackingPriorHandler; } void StreamlineTrackingFilter::GetSeedPointsFromSeedImage() { MITK_INFO << "StreamlineTracking - Calculating seed points."; m_SeedPoints.clear(); typedef ImageRegionConstIterator< ItkFloatImgType > MaskIteratorType; MaskIteratorType sit(m_SeedImage, m_SeedImage->GetLargestPossibleRegion()); sit.GoToBegin(); while (!sit.IsAtEnd()) { if (sit.Value()>0) { ItkFloatImgType::IndexType index = sit.GetIndex(); itk::ContinuousIndex start; start[0] = index[0]; start[1] = index[1]; start[2] = index[2]; itk::Point worldPos; m_SeedImage->TransformContinuousIndexToPhysicalPoint(start, worldPos); if ( mitk::imv::IsInsideMask(worldPos, m_InterpolateMasks, m_MaskInterpolator) ) { m_SeedPoints.push_back(worldPos); for (int s = 1; s < m_SeedsPerVoxel; s++) { start[0] = index[0] + m_TrackingHandler->GetRandDouble(-0.5, 0.5); start[1] = index[1] + m_TrackingHandler->GetRandDouble(-0.5, 0.5); start[2] = index[2] + m_TrackingHandler->GetRandDouble(-0.5, 0.5); itk::Point worldPos; m_SeedImage->TransformContinuousIndexToPhysicalPoint(start, worldPos); m_SeedPoints.push_back(worldPos); } } } ++sit; } } void StreamlineTrackingFilter::GenerateData() { this->BeforeTracking(); if (m_Random) std::random_shuffle(m_SeedPoints.begin(), m_SeedPoints.end()); m_CurrentTracts = 0; int num_seeds = m_SeedPoints.size(); itk::Index<3> zeroIndex; zeroIndex.Fill(0); m_Progress = 0; int i = 0; int print_interval = num_seeds/100; if (print_interval<100) m_Verbose=false; #pragma omp parallel while (i=num_seeds || m_StopTracking) continue; else if (m_Verbose && i%print_interval==0) #pragma omp critical { m_Progress += print_interval; std::cout << " \r"; if (m_MaxNumTracts>0) std::cout << "Tried: " << m_Progress << "/" << num_seeds << " | Accepted: " << m_CurrentTracts << "/" << m_MaxNumTracts << '\r'; else std::cout << "Tried: " << m_Progress << "/" << num_seeds << " | Accepted: " << m_CurrentTracts << '\r'; cout.flush(); } const itk::Point worldPos = m_SeedPoints.at(temp_i); for (unsigned int trials=0; trials dir; dir.fill(0.0); std::deque< vnl_vector_fixed > olddirs; dir = GetNewDirection(worldPos, olddirs, zeroIndex) * 0.5f; bool exclude = false; if (m_ExclusionRegions.IsNotNull() && mitk::imv::IsInsideMask(worldPos, m_InterpolateMasks, m_ExclusionInterpolator)) exclude = true; bool success = false; if (dir.magnitude()>0.0001 && !exclude) { // forward tracking tractLength = FollowStreamline(worldPos, dir, &fib, &direction_container, 0, false, exclude); fib.push_front(worldPos); // backward tracking if (!exclude) tractLength = FollowStreamline(worldPos, -dir, &fib, &direction_container, tractLength, true, exclude); counter = fib.size(); if (tractLength>=m_MinTractLength && counter>=2 && !exclude) { #pragma omp critical if ( IsValidFiber(&fib) ) { if (!m_StopTracking) { if (!m_UseOutputProbabilityMap) m_Tractogram.push_back(fib); else FiberToProbmap(&fib); m_CurrentTracts++; success = true; } if (m_MaxNumTracts > 0 && m_CurrentTracts>=static_cast(m_MaxNumTracts)) { if (!m_StopTracking) { std::cout << " \r"; MITK_INFO << "Reconstructed maximum number of tracts (" << m_CurrentTracts << "). Stopping tractography."; } m_StopTracking = true; } } } } if (success || m_TrackingHandler->GetMode()!=mitk::TrackingDataHandler::PROBABILISTIC) break; // we only try one seed point multiple times if we use a probabilistic tracker and have not found a valid streamline yet }// trials per seed }// seed points this->AfterTracking(); } bool StreamlineTrackingFilter::IsValidFiber(FiberType* fib) { if (m_EndpointConstraint==EndpointConstraints::NONE) { return true; } else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) && mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint EPS_IN_TARGET chosen!"; } else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_TARGET_LABELDIFF) { if (m_TargetImageSet) { float v1 = mitk::imv::GetImageValue(fib->front(), false, m_TargetInterpolator); float v2 = mitk::imv::GetImageValue(fib->back(), false, m_TargetInterpolator); if ( v1>0.0 && v2>0.0 && v1!=v2 ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint EPS_IN_TARGET_LABELDIFF chosen!"; } else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_SEED_AND_TARGET) { if (m_TargetImageSet && m_SeedImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_SeedInterpolator) && mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) ) return true; if ( mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_SeedInterpolator) && mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target or seed image set but endpoint constraint EPS_IN_SEED_AND_TARGET chosen!"; } else if (m_EndpointConstraint==EndpointConstraints::MIN_ONE_EP_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) || mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint MIN_ONE_EP_IN_TARGET chosen!"; } else if (m_EndpointConstraint==EndpointConstraints::ONE_EP_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) && !mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) ) return true; if ( !mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) && mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint ONE_EP_IN_TARGET chosen!"; } else if (m_EndpointConstraint==EndpointConstraints::NO_EP_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) || mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) ) return false; return true; } else mitkThrow() << "No target image set but endpoint constraint NO_EP_IN_TARGET chosen!"; } return true; } void StreamlineTrackingFilter::FiberToProbmap(FiberType* fib) { ItkDoubleImgType::IndexType last_idx; last_idx.Fill(0); for (auto p : *fib) { ItkDoubleImgType::IndexType idx; m_OutputProbabilityMap->TransformPhysicalPointToIndex(p, idx); if (idx != last_idx) { if (m_OutputProbabilityMap->GetLargestPossibleRegion().IsInside(idx)) m_OutputProbabilityMap->SetPixel(idx, m_OutputProbabilityMap->GetPixel(idx)+1); last_idx = idx; } } } void StreamlineTrackingFilter::BuildFibers(bool check) { if (m_BuildFibersReady::New(); vtkSmartPointer vNewLines = vtkSmartPointer::New(); vtkSmartPointer vNewPoints = vtkSmartPointer::New(); for (unsigned int i=0; i container = vtkSmartPointer::New(); FiberType fib = m_Tractogram.at(i); for (FiberType::iterator it = fib.begin(); it!=fib.end(); ++it) { vtkIdType id = vNewPoints->InsertNextPoint((*it).GetDataPointer()); container->GetPointIds()->InsertNextId(id); } vNewLines->InsertNextCell(container); } if (check) for (int i=0; iSetPoints(vNewPoints); m_FiberPolyData->SetLines(vNewLines); m_BuildFibersFinished = true; } void StreamlineTrackingFilter::AfterTracking() { if (m_Verbose) std::cout << " \r"; if (!m_UseOutputProbabilityMap) { MITK_INFO << "Reconstructed " << m_Tractogram.size() << " fibers."; MITK_INFO << "Generating polydata "; BuildFibers(false); } else { itk::RescaleIntensityImageFilter< ItkDoubleImgType, ItkDoubleImgType >::Pointer filter = itk::RescaleIntensityImageFilter< ItkDoubleImgType, ItkDoubleImgType >::New(); filter->SetInput(m_OutputProbabilityMap); filter->SetOutputMaximum(1.0); filter->SetOutputMinimum(0.0); filter->Update(); m_OutputProbabilityMap = filter->GetOutput(); } MITK_INFO << "done"; m_EndTime = std::chrono::system_clock::now(); std::chrono::hours hh = std::chrono::duration_cast(m_EndTime - m_StartTime); std::chrono::minutes mm = std::chrono::duration_cast(m_EndTime - m_StartTime); std::chrono::seconds ss = std::chrono::duration_cast(m_EndTime - m_StartTime); mm %= 60; ss %= 60; MITK_INFO << "Tracking took " << hh.count() << "h, " << mm.count() << "m and " << ss.count() << "s"; m_SeedPoints.clear(); - - if (m_TrackingPriorHandler!=nullptr) - delete m_TrackingPriorHandler; } void StreamlineTrackingFilter::SetDicomProperties(mitk::FiberBundle::Pointer fib) { std::string model_code_value = "-"; std::string model_code_meaning = "-"; std::string algo_code_value = "-"; std::string algo_code_meaning = "-"; if (m_TrackingHandler->GetMode()==mitk::TrackingDataHandler::DETERMINISTIC && dynamic_cast(m_TrackingHandler) && !m_TrackingHandler->GetInterpolate()) { algo_code_value = "sup181_ee04"; algo_code_meaning = "FACT"; } else if (m_TrackingHandler->GetMode()==mitk::TrackingDataHandler::DETERMINISTIC) { algo_code_value = "sup181_ee01"; algo_code_meaning = "Deterministic"; } else if (m_TrackingHandler->GetMode()==mitk::TrackingDataHandler::PROBABILISTIC) { algo_code_value = "sup181_ee02"; algo_code_meaning = "Probabilistic"; } if (dynamic_cast(m_TrackingHandler) || (dynamic_cast(m_TrackingHandler) && dynamic_cast(m_TrackingHandler)->GetIsOdfFromTensor() ) ) { if ( dynamic_cast(m_TrackingHandler) && dynamic_cast(m_TrackingHandler)->GetNumTensorImages()>1 ) { model_code_value = "sup181_bb02"; model_code_meaning = "Multi Tensor"; } else { model_code_value = "sup181_bb01"; model_code_meaning = "Single Tensor"; } } else if (dynamic_cast*>(m_TrackingHandler) || dynamic_cast*>(m_TrackingHandler)) { model_code_value = "sup181_bb03"; model_code_meaning = "Model Free"; } else if (dynamic_cast(m_TrackingHandler)) { model_code_value = "-"; model_code_meaning = "ODF"; } else if (dynamic_cast(m_TrackingHandler)) { model_code_value = "-"; model_code_meaning = "Peaks"; } fib->SetProperty("DICOM.anatomy.value", mitk::StringProperty::New("T-A0095")); fib->SetProperty("DICOM.anatomy.meaning", mitk::StringProperty::New("White matter of brain and spinal cord")); fib->SetProperty("DICOM.algo_code.value", mitk::StringProperty::New(algo_code_value)); fib->SetProperty("DICOM.algo_code.meaning", mitk::StringProperty::New(algo_code_meaning)); fib->SetProperty("DICOM.model_code.value", mitk::StringProperty::New(model_code_value)); fib->SetProperty("DICOM.model_code.meaning", mitk::StringProperty::New(model_code_meaning)); } } diff --git a/Modules/DiffusionImaging/FiberTracking/Testing/files.cmake b/Modules/DiffusionImaging/FiberTracking/Testing/files.cmake index 7190a886e0..6f08189677 100644 --- a/Modules/DiffusionImaging/FiberTracking/Testing/files.cmake +++ b/Modules/DiffusionImaging/FiberTracking/Testing/files.cmake @@ -1,14 +1,15 @@ SET(MODULE_CUSTOM_TESTS mitkFiberBundleReaderWriterTest.cpp mitkGibbsTrackingTest.cpp mitkStreamlineTractographyTest.cpp mitkLocalFiberPlausibilityTest.cpp mitkFiberTransformationTest.cpp mitkFiberExtractionTest.cpp mitkFiberGenerationTest.cpp mitkFiberfoxSignalGenerationTest.cpp mitkMachineLearningTrackingTest.cpp mitkFiberProcessingTest.cpp + mitkFiberFitTest.cpp ) diff --git a/Modules/DiffusionImaging/FiberTracking/Testing/mitkFiberFitTest.cpp b/Modules/DiffusionImaging/FiberTracking/Testing/mitkFiberFitTest.cpp new file mode 100644 index 0000000000..047423b45b --- /dev/null +++ b/Modules/DiffusionImaging/FiberTracking/Testing/mitkFiberFitTest.cpp @@ -0,0 +1,264 @@ +/*=================================================================== + +The Medical Imaging Interaction Toolkit (MITK) + +Copyright (c) German Cancer Research Center, +Division of Medical and Biological Informatics. +All rights reserved. + +This software is distributed WITHOUT ANY WARRANTY; without +even the implied warranty of MERCHANTABILITY or FITNESS FOR +A PARTICULAR PURPOSE. + +See LICENSE.txt or http://www.mitk.org for details. + +===================================================================*/ + +#include "mitkTestingMacros.h" +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +class mitkFiberFitTestSuite : public mitk::TestFixture +{ + + CPPUNIT_TEST_SUITE(mitkFiberFitTestSuite); + MITK_TEST(Fit1); + MITK_TEST(Fit2); + MITK_TEST(Fit3); + MITK_TEST(Fit4); + MITK_TEST(Fit5); + MITK_TEST(Fit6); + CPPUNIT_TEST_SUITE_END(); + + typedef itk::Image ItkFloatImgType; + +private: + + /** Members used inside the different (sub-)tests. All members are initialized via setUp().*/ + + typedef itk::FitFibersToImageFilter FitterType; + FitterType::Pointer fitter; + +public: + + mitk::FiberBundle::Pointer LoadFib(std::string fib_name) + { + std::vector fibInfile = mitk::IOUtil::Load(GetTestDataFilePath("DiffusionImaging/FiberFit/" + fib_name)); + mitk::BaseData::Pointer baseData = fibInfile.at(0); + mitk::FiberBundle::Pointer fib = dynamic_cast(baseData.GetPointer()); + return fib; + } + + void setUp() override + { + std::vector tracts; + tracts.push_back(LoadFib("Cluster_0.fib")); + tracts.push_back(LoadFib("Cluster_1.fib")); + tracts.push_back(LoadFib("Cluster_2.fib")); + tracts.push_back(LoadFib("Cluster_3.fib")); + tracts.push_back(LoadFib("Cluster_4.fib")); + + mitk::PreferenceListReaderOptionsFunctor functor = mitk::PreferenceListReaderOptionsFunctor({"Peak Image"}, {}); + mitk::PeakImage::Pointer peaks = dynamic_cast(mitk::IOUtil::Load(GetTestDataFilePath("DiffusionImaging/FiberFit/csd_peak_image.nii.gz"), &functor)[0].GetPointer()); + + + typedef mitk::ImageToItk< mitk::PeakImage::ItkPeakImageType > CasterType; + CasterType::Pointer caster = CasterType::New(); + caster->SetInput(peaks); + caster->Update(); + mitk::PeakImage::ItkPeakImageType::Pointer peak_image = caster->GetOutput(); + + fitter = FitterType::New(); + fitter->SetPeakImage(peak_image); + fitter->SetTractograms(tracts); + } + + void tearDown() override + { + + } + + void CompareFibs(mitk::FiberBundle::Pointer test, mitk::FiberBundle::Pointer ref, std::string out_name) + { + vtkSmartPointer weights = test->GetFiberWeights(); + vtkSmartPointer ref_weights = ref->GetFiberWeights(); + + CPPUNIT_ASSERT_MESSAGE("Number of weights should be equal", weights->GetSize()==ref_weights->GetSize()); + + for (int i=0; iGetSize(); ++i) + { + if (ref_weights->GetValue(i)>0) + { + if (fabs( weights->GetValue(i)/ref_weights->GetValue(i)-1 )>0.01) + { + mitk::IOUtil::Save(test, mitk::IOUtil::GetTempPath()+out_name); + CPPUNIT_ASSERT_MESSAGE("Weights should be equal", false); + } + } + else if (weights->GetValue(i)>0) + { + mitk::IOUtil::Save(test, mitk::IOUtil::GetTempPath()+out_name); + CPPUNIT_ASSERT_MESSAGE("Weights should be equal", false); + } + } + } + + void CompareImages(mitk::PeakImage::ItkPeakImageType::Pointer testImage, std::string name) + { + typedef mitk::ImageToItk< mitk::PeakImage::ItkPeakImageType > CasterType; + CasterType::Pointer caster = CasterType::New(); + caster->SetInput(mitk::IOUtil::LoadImage(GetTestDataFilePath("DiffusionImaging/FiberFit/out/" + name))); + caster->Update(); + mitk::PeakImage::ItkPeakImageType::Pointer refImage = caster->GetOutput(); + + + itk::ImageRegionConstIterator< mitk::PeakImage::ItkPeakImageType > it1(testImage, testImage->GetLargestPossibleRegion()); + itk::ImageRegionConstIterator< mitk::PeakImage::ItkPeakImageType > it2(refImage, refImage->GetLargestPossibleRegion()); + + while(!it1.IsAtEnd()) + { + if (it2.Get()>0.0001) + { + if (fabs( it1.Get()/it2.Get()-1 )>0.01) + { + itk::ImageFileWriter< mitk::PeakImage::ItkPeakImageType >::Pointer writer = itk::ImageFileWriter< mitk::PeakImage::ItkPeakImageType >::New(); + writer->SetInput(testImage); + writer->SetFileName(mitk::IOUtil::GetTempPath()+name); + writer->Update(); + + MITK_INFO << it1.Get() << " - " << it2.Get(); + + CPPUNIT_ASSERT_MESSAGE("Peak images should be equal 1", false); + } + } + else if (it1.Get()>0.0001) + { + itk::ImageFileWriter< mitk::PeakImage::ItkPeakImageType >::Pointer writer = itk::ImageFileWriter< mitk::PeakImage::ItkPeakImageType >::New(); + writer->SetInput(testImage); + writer->SetFileName(mitk::IOUtil::GetTempPath()+name); + writer->Update(); + + CPPUNIT_ASSERT_MESSAGE("Peak images should be equal 2", false); + } + + ++it1; + ++it2; + } + } + + void Fit1() + { + omp_set_num_threads(1); + fitter->SetLambda(0.1); + fitter->SetFilterOutliers(false); + fitter->SetRegularization(VnlCostFunction::NONE); + fitter->Update(); + + std::vector< mitk::FiberBundle::Pointer > output_tracts = fitter->GetTractograms(); + mitk::FiberBundle::Pointer test = mitk::FiberBundle::New(); + test = test->AddBundles(output_tracts); + mitk::FiberBundle::Pointer ref = LoadFib("out/NONE_fitted.fib"); + CompareFibs(test, ref, "NONE_fitted.fib"); + CompareImages(fitter->GetFittedImage(), "NONE_fitted_image.nrrd"); + CompareImages(fitter->GetResidualImage(), "NONE_residual_image.nrrd"); + } + + void Fit2() + { + omp_set_num_threads(1); + fitter->SetLambda(0.1); + fitter->SetFilterOutliers(false); + fitter->SetRegularization(VnlCostFunction::MSM); + fitter->Update(); + + std::vector< mitk::FiberBundle::Pointer > output_tracts = fitter->GetTractograms(); + mitk::FiberBundle::Pointer test = mitk::FiberBundle::New(); + test = test->AddBundles(output_tracts); + mitk::FiberBundle::Pointer ref = LoadFib("out/MSM_fitted.fib"); + CompareFibs(test, ref, "MSM_fitted.fib"); + CompareImages(fitter->GetFittedImage(), "MSM_fitted_image.nrrd"); + CompareImages(fitter->GetResidualImage(), "MSM_residual_image.nrrd"); + } + + void Fit3() + { + omp_set_num_threads(1); + fitter->SetLambda(0.1); + fitter->SetFilterOutliers(false); + fitter->SetRegularization(VnlCostFunction::VARIANCE); + fitter->Update(); + + std::vector< mitk::FiberBundle::Pointer > output_tracts = fitter->GetTractograms(); + mitk::FiberBundle::Pointer test = mitk::FiberBundle::New(); + test = test->AddBundles(output_tracts); + mitk::FiberBundle::Pointer ref = LoadFib("out/MSE_fitted.fib"); + CompareFibs(test, ref, "MSE_fitted.fib"); + CompareImages(fitter->GetFittedImage(), "MSE_fitted_image.nrrd"); + CompareImages(fitter->GetResidualImage(), "MSE_residual_image.nrrd"); + } + + void Fit4() + { + omp_set_num_threads(1); + fitter->SetLambda(0.1); + fitter->SetFilterOutliers(false); + fitter->SetRegularization(VnlCostFunction::VOXEL_VARIANCE); + fitter->Update(); + + std::vector< mitk::FiberBundle::Pointer > output_tracts = fitter->GetTractograms(); + mitk::FiberBundle::Pointer test = mitk::FiberBundle::New(); + test = test->AddBundles(output_tracts); + mitk::FiberBundle::Pointer ref = LoadFib("out/LocalMSE_fitted.fib"); + CompareFibs(test, ref, "LocalMSE_fitted.fib"); + CompareImages(fitter->GetFittedImage(), "LocalMSE_fitted_image.nrrd"); + CompareImages(fitter->GetResidualImage(), "LocalMSE_residual_image.nrrd"); + } + + void Fit5() + { + omp_set_num_threads(1); + fitter->SetLambda(0.1); + fitter->SetFilterOutliers(false); + fitter->SetRegularization(VnlCostFunction::GROUP_VARIANCE); + fitter->Update(); + + std::vector< mitk::FiberBundle::Pointer > output_tracts = fitter->GetTractograms(); + mitk::FiberBundle::Pointer test = mitk::FiberBundle::New(); + test = test->AddBundles(output_tracts); + mitk::FiberBundle::Pointer ref = LoadFib("out/GroupMSE_fitted.fib"); + CompareFibs(test, ref, "GroupMSE_fitted.fib"); + CompareImages(fitter->GetFittedImage(), "GroupMSE_fitted_image.nrrd"); + CompareImages(fitter->GetResidualImage(), "GroupMSE_residual_image.nrrd"); + } + + void Fit6() + { + omp_set_num_threads(1); + fitter->SetLambda(10); + fitter->SetFilterOutliers(false); + fitter->SetRegularization(VnlCostFunction::GROUP_LASSO); + fitter->Update(); + + std::vector< mitk::FiberBundle::Pointer > output_tracts = fitter->GetTractograms(); + mitk::FiberBundle::Pointer test = mitk::FiberBundle::New(); + test = test->AddBundles(output_tracts); + mitk::FiberBundle::Pointer ref = LoadFib("out/GroupLasso_fitted.fib"); + CompareFibs(test, ref, "GroupLasso_fitted.fib"); + CompareImages(fitter->GetFittedImage(), "GroupLasso_fitted_image.nrrd"); + CompareImages(fitter->GetResidualImage(), "GroupLasso_residual_image.nrrd"); + } + +}; + +MITK_TEST_SUITE_REGISTRATION(mitkFiberFit) diff --git a/Modules/DiffusionImaging/FiberTracking/cmdapps/FiberProcessing/FitFibersToImage.cpp b/Modules/DiffusionImaging/FiberTracking/cmdapps/FiberProcessing/FitFibersToImage.cpp index 19900af993..7afad23cf4 100755 --- a/Modules/DiffusionImaging/FiberTracking/cmdapps/FiberProcessing/FitFibersToImage.cpp +++ b/Modules/DiffusionImaging/FiberTracking/cmdapps/FiberProcessing/FitFibersToImage.cpp @@ -1,265 +1,350 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include #include #include #include #include #include #include #include #include #include #include +#include +#include typedef itksys::SystemTools ist; typedef itk::Point PointType4; typedef itk::Image< float, 4 > PeakImgType; std::vector< std::string > get_file_list(const std::string& path) { std::vector< std::string > file_list; itk::Directory::Pointer dir = itk::Directory::New(); if (dir->Load(path.c_str())) { int n = dir->GetNumberOfFiles(); for (int r = 0; r < n; r++) { const char *filename = dir->GetFile(r); std::string ext = ist::GetFilenameExtension(filename); if (ext==".fib" || ext==".trk") file_list.push_back(path + '/' + filename); } } return file_list; } /*! \brief Fits the tractogram to the input peak image by assigning a weight to each fiber (similar to https://doi.org/10.1016/j.neuroimage.2015.06.092). */ int main(int argc, char* argv[]) { mitkCommandLineParser parser; parser.setTitle("Fit Fibers To Image"); parser.setCategory("Fiber Tracking and Processing Methods"); parser.setDescription("Assigns a weight to each fiber in order to optimally explain the input peak image"); parser.setContributor("MIC"); parser.setArgumentPrefix("--", "-"); parser.addArgument("", "i1", mitkCommandLineParser::StringList, "Input tractograms:", "input tractograms (.fib, vtk ascii file format)", us::Any(), false); - parser.addArgument("", "i2", mitkCommandLineParser::InputFile, "Input peaks:", "input peak image", us::Any(), false); + parser.addArgument("", "i2", mitkCommandLineParser::InputFile, "Input image:", "input image", us::Any(), false); parser.addArgument("", "o", mitkCommandLineParser::OutputDirectory, "Output:", "output root", us::Any(), false); parser.addArgument("max_iter", "", mitkCommandLineParser::Int, "Max. iterations:", "maximum number of optimizer iterations", 20); parser.addArgument("bundle_based", "", mitkCommandLineParser::Bool, "Bundle based fit:", "fit one weight per input tractogram/bundle, not for each fiber", false); parser.addArgument("min_g", "", mitkCommandLineParser::Float, "Min. g:", "lower termination threshold for gradient magnitude", 1e-5); parser.addArgument("lambda", "", mitkCommandLineParser::Float, "Lambda:", "modifier for regularization", 0.1); parser.addArgument("save_res", "", mitkCommandLineParser::Bool, "Save Residuals:", "save residual images", false); parser.addArgument("save_weights", "", mitkCommandLineParser::Bool, "Save Weights:", "save fiber weights in a separate text file", false); - parser.addArgument("dont_filter_outliers", "", mitkCommandLineParser::Bool, "Don't filter outliers:", "don't perform second optimization run with an upper weight bound based on the first weight estimation (95% quantile)", false); + parser.addArgument("filter_outliers", "", mitkCommandLineParser::Bool, "Filter outliers:", "perform second optimization run with an upper weight bound based on the first weight estimation (99% quantile)", false); parser.addArgument("join_tracts", "", mitkCommandLineParser::Bool, "Join output tracts:", "outout tracts are merged into a single tractogram", false); - parser.addArgument("regu", "", mitkCommandLineParser::String, "Regularization:", "MSM, MSE, LocalMSE (default), GroupLasso, GroupMSE, NONE"); + parser.addArgument("regu", "", mitkCommandLineParser::String, "Regularization:", "MSM, Variance, VoxelVariance (default), Lasso, GroupLasso, GroupVariance, NONE"); std::map parsedArgs = parser.parseArguments(argc, argv); if (parsedArgs.size()==0) return EXIT_FAILURE; mitkCommandLineParser::StringContainerType fib_files = us::any_cast(parsedArgs["i1"]); - std::string peak_file_name = us::any_cast(parsedArgs["i2"]); + std::string input_image_name = us::any_cast(parsedArgs["i2"]); std::string outRoot = us::any_cast(parsedArgs["o"]); bool single_fib = true; if (parsedArgs.count("bundle_based")) single_fib = !us::any_cast(parsedArgs["bundle_based"]); bool save_residuals = false; if (parsedArgs.count("save_res")) save_residuals = us::any_cast(parsedArgs["save_res"]); bool save_weights = false; if (parsedArgs.count("save_weights")) save_weights = us::any_cast(parsedArgs["save_weights"]); - std::string regu = "LocalMSE"; + std::string regu = "VoxelVariance"; if (parsedArgs.count("regu")) regu = us::any_cast(parsedArgs["regu"]); bool join_tracts = false; if (parsedArgs.count("join_tracts")) join_tracts = us::any_cast(parsedArgs["join_tracts"]); int max_iter = 20; if (parsedArgs.count("max_iter")) max_iter = us::any_cast(parsedArgs["max_iter"]); float g_tol = 1e-5; if (parsedArgs.count("min_g")) g_tol = us::any_cast(parsedArgs["min_g"]); float lambda = 0.1; if (parsedArgs.count("lambda")) lambda = us::any_cast(parsedArgs["lambda"]); - bool filter_outliers = true; - if (parsedArgs.count("dont_filter_outliers")) - filter_outliers = !us::any_cast(parsedArgs["dont_filter_outliers"]); + bool filter_outliers = false; + if (parsedArgs.count("filter_outliers")) + filter_outliers = us::any_cast(parsedArgs["filter_outliers"]); try { MITK_INFO << "Loading data"; std::streambuf *old = cout.rdbuf(); // <-- save std::stringstream ss; std::cout.rdbuf (ss.rdbuf()); // <-- redirect std::vector< mitk::FiberBundle::Pointer > input_tracts; mitk::PreferenceListReaderOptionsFunctor functor = mitk::PreferenceListReaderOptionsFunctor({"Peak Image", "Fiberbundles"}, {}); - mitk::Image::Pointer inputImage = dynamic_cast(mitk::IOUtil::Load(peak_file_name, &functor)[0].GetPointer()); - - typedef mitk::ImageToItk< PeakImgType > CasterType; - CasterType::Pointer caster = CasterType::New(); - caster->SetInput(inputImage); - caster->Update(); - PeakImgType::Pointer peak_image = caster->GetOutput(); std::vector< std::string > fib_names; for (auto item : fib_files) { if ( ist::FileIsDirectory(item) ) { for ( auto fibFile : get_file_list(item) ) { mitk::FiberBundle::Pointer inputTractogram = dynamic_cast(mitk::IOUtil::Load(fibFile)[0].GetPointer()); if (inputTractogram.IsNull()) continue; input_tracts.push_back(inputTractogram); fib_names.push_back(fibFile); } } else { mitk::FiberBundle::Pointer inputTractogram = dynamic_cast(mitk::IOUtil::Load(item)[0].GetPointer()); if (inputTractogram.IsNull()) continue; input_tracts.push_back(inputTractogram); fib_names.push_back(item); } } std::cout.rdbuf (old); // <-- restore itk::FitFibersToImageFilter::Pointer fitter = itk::FitFibersToImageFilter::New(); - fitter->SetPeakImage(peak_image); + + mitk::BaseData::Pointer inputData = mitk::IOUtil::Load(input_image_name, &functor)[0].GetPointer(); + mitk::Image::Pointer mitk_image = dynamic_cast(inputData.GetPointer()); + mitk::PeakImage::Pointer mitk_peak_image = dynamic_cast(inputData.GetPointer()); + if (mitk_peak_image.IsNotNull()) + { + typedef mitk::ImageToItk< mitk::PeakImage::ItkPeakImageType > CasterType; + CasterType::Pointer caster = CasterType::New(); + caster->SetInput(mitk_peak_image); + caster->Update(); + mitk::PeakImage::ItkPeakImageType::Pointer peak_image = caster->GetOutput(); + fitter->SetPeakImage(peak_image); + } + else if (mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(mitk_image)) + { + fitter->SetDiffImage(mitk::DiffusionPropertyHelper::GetItkVectorImage(mitk_image)); + mitk::TensorModel<>* model = new mitk::TensorModel<>(); + model->SetBvalue(1000); + model->SetDiffusivity1(0.0010); + model->SetDiffusivity2(0.00015); + model->SetDiffusivity3(0.00015); + model->SetGradientList(mitk::DiffusionPropertyHelper::GetGradientContainer(mitk_image)); + fitter->SetSignalModel(model); + } + else if (mitk_image->GetDimension()==3) + { + itk::FitFibersToImageFilter::DoubleImgType::Pointer scalar_image = itk::FitFibersToImageFilter::DoubleImgType::New(); + mitk::CastToItkImage(mitk_image, scalar_image); + fitter->SetScalarImage(scalar_image); + } + else + { + MITK_INFO << "Input image invalid. Valid options are peak image, 3D scalar image or raw diffusion-weighted image."; + return EXIT_FAILURE; + } + fitter->SetTractograms(input_tracts); fitter->SetFitIndividualFibers(single_fib); fitter->SetMaxIterations(max_iter); fitter->SetGradientTolerance(g_tol); fitter->SetLambda(lambda); fitter->SetFilterOutliers(filter_outliers); if (regu=="MSM") fitter->SetRegularization(VnlCostFunction::REGU::MSM); - else if (regu=="MSE") - fitter->SetRegularization(VnlCostFunction::REGU::MSE); - else if (regu=="Local_MSE") - fitter->SetRegularization(VnlCostFunction::REGU::Local_MSE); + else if (regu=="Variance") + fitter->SetRegularization(VnlCostFunction::REGU::VARIANCE); + else if (regu=="Lasso") + fitter->SetRegularization(VnlCostFunction::REGU::LASSO); + else if (regu=="VoxelVariance") + fitter->SetRegularization(VnlCostFunction::REGU::VOXEL_VARIANCE); else if (regu=="GroupLasso") fitter->SetRegularization(VnlCostFunction::REGU::GROUP_LASSO); - else if (regu=="GroupMSE") - fitter->SetRegularization(VnlCostFunction::REGU::GROUP_MSE); + else if (regu=="GroupVariance") + fitter->SetRegularization(VnlCostFunction::REGU::GROUP_VARIANCE); else if (regu=="NONE") fitter->SetRegularization(VnlCostFunction::REGU::NONE); fitter->Update(); - if (save_residuals) + if (save_residuals && mitk_peak_image.IsNotNull()) { itk::ImageFileWriter< PeakImgType >::Pointer writer = itk::ImageFileWriter< PeakImgType >::New(); writer->SetInput(fitter->GetFittedImage()); - writer->SetFileName(outRoot + "fitted_image.nrrd"); + writer->SetFileName(outRoot + "_fitted.nii.gz"); writer->Update(); writer->SetInput(fitter->GetResidualImage()); - writer->SetFileName(outRoot + "residual_image.nrrd"); + writer->SetFileName(outRoot + "_residual.nii.gz"); writer->Update(); writer->SetInput(fitter->GetOverexplainedImage()); - writer->SetFileName(outRoot + "overexplained_image.nrrd"); + writer->SetFileName(outRoot + "_overexplained.nii.gz"); writer->Update(); writer->SetInput(fitter->GetUnderexplainedImage()); - writer->SetFileName(outRoot + "underexplained_image.nrrd"); + writer->SetFileName(outRoot + "_underexplained.nii.gz"); + writer->Update(); + } + else if (save_residuals && mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(mitk_image)) + { + { + mitk::Image::Pointer outImage = mitk::GrabItkImageMemory( fitter->GetFittedImageDiff().GetPointer() ); + mitk::DiffusionPropertyHelper::CopyProperties(mitk_image, outImage, true); + mitk::DiffusionPropertyHelper propertyHelper( outImage ); + propertyHelper.InitializeImage(); + mitk::IOUtil::Save(outImage, "application/vnd.mitk.nii.gz", outRoot + "_fitted_image.nii.gz"); + } + + { + mitk::Image::Pointer outImage = mitk::GrabItkImageMemory( fitter->GetResidualImageDiff().GetPointer() ); + mitk::DiffusionPropertyHelper::CopyProperties(mitk_image, outImage, true); + mitk::DiffusionPropertyHelper propertyHelper( outImage ); + propertyHelper.InitializeImage(); + mitk::IOUtil::Save(outImage, "application/vnd.mitk.nii.gz", outRoot + "_residual_image.nii.gz"); + } + + { + mitk::Image::Pointer outImage = mitk::GrabItkImageMemory( fitter->GetOverexplainedImageDiff().GetPointer() ); + mitk::DiffusionPropertyHelper::CopyProperties(mitk_image, outImage, true); + mitk::DiffusionPropertyHelper propertyHelper( outImage ); + propertyHelper.InitializeImage(); + mitk::IOUtil::Save(outImage, "application/vnd.mitk.nii.gz", outRoot + "_overexplained_image.nii.gz"); + } + + { + mitk::Image::Pointer outImage = mitk::GrabItkImageMemory( fitter->GetUnderexplainedImageDiff().GetPointer() ); + mitk::DiffusionPropertyHelper::CopyProperties(mitk_image, outImage, true); + mitk::DiffusionPropertyHelper propertyHelper( outImage ); + propertyHelper.InitializeImage(); + mitk::IOUtil::Save(outImage, "application/vnd.mitk.nii.gz", outRoot + "_underexplained_image.nii.gz"); + } + } + else if (save_residuals) + { + itk::ImageFileWriter< itk::FitFibersToImageFilter::DoubleImgType >::Pointer writer = itk::ImageFileWriter< itk::FitFibersToImageFilter::DoubleImgType >::New(); + writer->SetInput(fitter->GetFittedImageScalar()); + writer->SetFileName(outRoot + "_fitted_image.nii.gz"); + writer->Update(); + + writer->SetInput(fitter->GetResidualImageScalar()); + writer->SetFileName(outRoot + "_residual_image.nii.gz"); + writer->Update(); + + writer->SetInput(fitter->GetOverexplainedImageScalar()); + writer->SetFileName(outRoot + "_overexplained_image.nii.gz"); + writer->Update(); + + writer->SetInput(fitter->GetUnderexplainedImageScalar()); + writer->SetFileName(outRoot + "_underexplained_image.nii.gz"); writer->Update(); } std::vector< mitk::FiberBundle::Pointer > output_tracts = fitter->GetTractograms(); if (!join_tracts) { for (unsigned int bundle=0; bundleGetNumFibers(); ++f) logfile << output_tracts.at(bundle)->GetFiberWeight(f) << "\n"; logfile.close(); } } } else { mitk::FiberBundle::Pointer out = mitk::FiberBundle::New(); out = out->AddBundles(output_tracts); out->ColorFibersByFiberWeights(false, true); mitk::IOUtil::Save(out, outRoot + "_fitted.fib"); if (save_weights) { ofstream logfile; logfile.open (outRoot + "_weights.txt"); for (int f=0; fGetNumFibers(); ++f) logfile << out->GetFiberWeight(f) << "\n"; logfile.close(); } } } catch (itk::ExceptionObject e) { std::cout << e; return EXIT_FAILURE; } catch (std::exception e) { std::cout << e.what(); return EXIT_FAILURE; } catch (...) { std::cout << "ERROR!?!"; return EXIT_FAILURE; } return EXIT_SUCCESS; } diff --git a/Modules/DiffusionImaging/FiberTracking/cmdapps/Fiberfox/FiberfoxOptimization.cpp b/Modules/DiffusionImaging/FiberTracking/cmdapps/Fiberfox/FiberfoxOptimization.cpp index 038261fc82..bcf427d008 100755 --- a/Modules/DiffusionImaging/FiberTracking/cmdapps/Fiberfox/FiberfoxOptimization.cpp +++ b/Modules/DiffusionImaging/FiberTracking/cmdapps/Fiberfox/FiberfoxOptimization.cpp @@ -1,604 +1,604 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include #include #include #include #include #include "mitkCommandLineParser.h" #include #include #include #include #include #include #include #include #include #include using namespace mitk; double CalcErrorSignal(itk::VectorImage< short, 3 >* reference, itk::VectorImage< short, 3 >* simulation, itk::Image< unsigned char,3 >::Pointer mask) { typedef itk::VectorImage< short, 3 > DwiImageType; try { itk::ImageRegionIterator< DwiImageType > it1(reference, reference->GetLargestPossibleRegion()); itk::ImageRegionIterator< DwiImageType > it2(simulation, simulation->GetLargestPossibleRegion()); unsigned int count = 0; double error = 0; while(!it1.IsAtEnd()) { if (mask.IsNull() || (mask.IsNotNull() && mask->GetLargestPossibleRegion().IsInside(it1.GetIndex()) && mask->GetPixel(it1.GetIndex())>0) ) { for (unsigned int i=0; iGetVectorLength(); ++i) { if (it1.Get()[i]>0) { double diff = (double)it2.Get()[i]/it1.Get()[i] - 1.0; error += fabs(diff); count++; } } } ++it1; ++it2; } return error/count; } catch(...) { return -1; } return -1; } double CalcErrorFA(const std::vector& histo_mod, mitk::Image::Pointer dwi1, itk::VectorImage< short, 3 >* dwi2, itk::Image< unsigned char,3 >::Pointer mask, itk::Image< double,3 >::Pointer fa1, itk::Image< double,3 >::Pointer md1) { typedef itk::TensorDerivedMeasurementsFilter MeasurementsType; typedef itk::Image< double, 3 > DoubleImageType; typedef itk::DiffusionTensor3DReconstructionImageFilter TensorReconstructionImageFilterType; DoubleImageType::Pointer fa2; { mitk::DiffusionPropertyHelper::GradientDirectionsContainerType::Pointer gradientContainerCopy = mitk::DiffusionPropertyHelper::GradientDirectionsContainerType::New(); for(auto it = mitk::DiffusionPropertyHelper::GetGradientContainer(dwi1)->Begin(); it != mitk::DiffusionPropertyHelper::GetGradientContainer(dwi1)->End(); it++) gradientContainerCopy->push_back(it.Value()); TensorReconstructionImageFilterType::Pointer tensorReconstructionFilter = TensorReconstructionImageFilterType::New(); tensorReconstructionFilter->SetBValue( mitk::DiffusionPropertyHelper::GetReferenceBValue(dwi1) ); tensorReconstructionFilter->SetGradientImage(gradientContainerCopy, dwi2 ); tensorReconstructionFilter->Update(); MeasurementsType::Pointer measurementsCalculator = MeasurementsType::New(); measurementsCalculator->SetInput( tensorReconstructionFilter->GetOutput() ); measurementsCalculator->SetMeasure(MeasurementsType::FA); measurementsCalculator->Update(); fa2 = measurementsCalculator->GetOutput(); } DoubleImageType::Pointer md2; if (md1.IsNotNull()) { typedef itk::AdcImageFilter< short, double > AdcFilterType; AdcFilterType::Pointer filter = AdcFilterType::New(); filter->SetInput( dwi2 ); filter->SetGradientDirections( mitk::DiffusionPropertyHelper::GetGradientContainer(dwi1) ); filter->SetB_value( mitk::DiffusionPropertyHelper::GetReferenceBValue(dwi1) ); filter->SetFitSignal(false); filter->Update(); md2 = filter->GetOutput(); } itk::ImageRegionConstIterator< DoubleImageType > it1(fa1, fa1->GetLargestPossibleRegion()); itk::ImageRegionConstIterator< DoubleImageType > it2(fa2, fa2->GetLargestPossibleRegion()); unsigned int count = 0; double error = 0; if (md1.IsNotNull() && md2.IsNotNull()) { itk::ImageRegionConstIterator< DoubleImageType > it3(md1, md1->GetLargestPossibleRegion()); itk::ImageRegionConstIterator< DoubleImageType > it4(md2, md2->GetLargestPossibleRegion()); while(!it1.IsAtEnd()) { if (mask.IsNull() || (mask.IsNotNull() && mask->GetLargestPossibleRegion().IsInside(it1.GetIndex()) && mask->GetPixel(it1.GetIndex())>0) ) { double fa = it1.Get(); if (fa>0 && it3.Get()>0) { double mod = 1.0; for (int i=histo_mod.size()-1; i>=0; --i) if (fa >= (double)i/histo_mod.size()) { mod = histo_mod.at(i); break; } double fa_diff = fabs(it2.Get()/fa - 1.0); double md_diff = fabs(it4.Get()/it3.Get() - 1.0); - error += mod * (fa_diff + md_diff); + error += mod*mod * (fa_diff + md_diff); count += 2; } } ++it1; ++it2; ++it3; ++it4; } } else { unsigned int count = 0; double error = 0; while(!it1.IsAtEnd()) { if (mask.IsNull() || (mask.IsNotNull() && mask->GetLargestPossibleRegion().IsInside(it1.GetIndex()) && mask->GetPixel(it1.GetIndex())>0) ) { double fa = it1.Get(); if (fa>0) { double mod = 1.0; for (int i=histo_mod.size()-1; i>=0; --i) if (fa >= (double)i/histo_mod.size()) { mod = histo_mod.at(i); break; } double fa_diff = fabs(it2.Get()/fa - 1.0); error += mod * fa_diff; ++count; } } ++it1; ++it2; } } return error/count; } FiberfoxParameters MakeProposalRelaxation(FiberfoxParameters old_params, double temperature) { std::random_device r; std::default_random_engine randgen(r()); std::uniform_int_distribution uint1(0, 4); FiberfoxParameters new_params(old_params); int prop = uint1(randgen); switch(prop) { case 0: { std::normal_distribution normal_dist(0, new_params.m_SignalGen.m_SignalScale*0.1*temperature); double add = 0; while (add == 0) add = normal_dist(randgen); new_params.m_SignalGen.m_SignalScale += add; MITK_INFO << "Proposal Signal Scale: " << new_params.m_SignalGen.m_SignalScale << " (" << add << ")"; break; } case 1: { int model_index = rand()%new_params.m_NonFiberModelList.size(); double t2 = new_params.m_NonFiberModelList[model_index]->GetT2(); std::normal_distribution normal_dist(0, t2*0.1*temperature); double add = 0; while (add == 0) add = normal_dist(randgen); t2 += add; new_params.m_NonFiberModelList[model_index]->SetT2(t2); MITK_INFO << "Proposal T2 (Non-Fiber " << model_index << "): " << t2 << " (" << add << ")"; break; } case 2: { int model_index = rand()%new_params.m_FiberModelList.size(); double t2 = new_params.m_FiberModelList[model_index]->GetT2(); std::normal_distribution normal_dist(0, t2*0.1*temperature); double add = 0; while (add == 0) add = normal_dist(randgen); t2 += add; new_params.m_FiberModelList[model_index]->SetT2(t2); MITK_INFO << "Proposal T2 (Fiber " << model_index << "): " << t2 << " (" << add << ")"; break; } case 3: { int model_index = rand()%new_params.m_NonFiberModelList.size(); double t1 = new_params.m_NonFiberModelList[model_index]->GetT1(); std::normal_distribution normal_dist(0, t1*0.1*temperature); double add = 0; while (add == 0) add = normal_dist(randgen); t1 += add; new_params.m_NonFiberModelList[model_index]->SetT1(t1); MITK_INFO << "Proposal T1 (Non-Fiber " << model_index << "): " << t1 << " (" << add << ")"; break; } case 4: { int model_index = rand()%new_params.m_FiberModelList.size(); double t1 = new_params.m_FiberModelList[model_index]->GetT1(); std::normal_distribution normal_dist(0, t1*0.1*temperature); double add = 0; while (add == 0) add = normal_dist(randgen); t1 += add; new_params.m_FiberModelList[model_index]->SetT1(t1); MITK_INFO << "Proposal T1 (Fiber " << model_index << "): " << t1 << " (" << add << ")"; break; } } return new_params; } double UpdateDiffusivity(double d, double temperature) { std::random_device r; std::default_random_engine randgen(r()); std::normal_distribution normal_dist(0, d*0.1*temperature); double add = 0; while (add == 0) add = normal_dist(randgen); if (d+add > 0.0025) d -= add; else if ( d+add < 0.0 ) d -= add; else d += add; return d; } void ProposeDiffusivities(mitk::DiffusionSignalModel<>* signalModel, double temperature) { if (dynamic_cast*>(signalModel)) { mitk::StickModel<>* m = dynamic_cast*>(signalModel); double new_d = UpdateDiffusivity(m->GetDiffusivity(), temperature); MITK_INFO << "d: " << new_d << " (" << new_d-m->GetDiffusivity() << ")"; m->SetDiffusivity(new_d); } else if (dynamic_cast*>(signalModel)) { mitk::TensorModel<>* m = dynamic_cast*>(signalModel); double new_d1 = UpdateDiffusivity(m->GetDiffusivity1(), temperature); double new_d2 = UpdateDiffusivity(m->GetDiffusivity2(), temperature); while (new_d1GetDiffusivity2(), temperature); MITK_INFO << "d1: " << new_d1 << " (" << new_d1-m->GetDiffusivity1() << ")"; MITK_INFO << "d2: " << new_d2 << " (" << new_d2-m->GetDiffusivity2() << ")"; m->SetDiffusivity1(new_d1); m->SetDiffusivity2(new_d2); m->SetDiffusivity3(new_d2); } else if (dynamic_cast*>(signalModel)) { mitk::BallModel<>* m = dynamic_cast*>(signalModel); double new_d = UpdateDiffusivity(m->GetDiffusivity(), temperature); MITK_INFO << "d: " << new_d << " (" << new_d-m->GetDiffusivity() << ")"; m->SetDiffusivity(new_d); } else if (dynamic_cast*>(signalModel)) { mitk::AstroStickModel<>* m = dynamic_cast*>(signalModel); double new_d = UpdateDiffusivity(m->GetDiffusivity(), temperature); MITK_INFO << "d: " << new_d << " (" << new_d-m->GetDiffusivity() << ")"; m->SetDiffusivity(new_d); } } FiberfoxParameters MakeProposalDiff(FiberfoxParameters old_params, double temperature) { FiberfoxParameters new_params(old_params); std::random_device r; std::default_random_engine randgen(r()); std::uniform_int_distribution uint1(0, new_params.m_NonFiberModelList.size() + new_params.m_FiberModelList.size() - 1); unsigned int prop = uint1(randgen); if (prop parsedArgs = parser.parseArguments(argc, argv); if (parsedArgs.size()==0) return EXIT_FAILURE; std::string paramName = us::any_cast(parsedArgs["parameters"]); std::string input = us::any_cast(parsedArgs["input"]); int iterations=1000; if (parsedArgs.count("iterations")) iterations = us::any_cast(parsedArgs["iterations"]); float start_temp=1.0; if (parsedArgs.count("start_temp")) start_temp = us::any_cast(parsedArgs["start_temp"]); float end_temp=0.1; if (parsedArgs.count("end_temp")) end_temp = us::any_cast(parsedArgs["end_temp"]); bool no_diff=false; if (parsedArgs.count("no_diff")) no_diff = true; bool no_relax=false; if (parsedArgs.count("no_relax")) no_relax = true; std::string fa_file = ""; if (parsedArgs.count("fa_image")) fa_file = us::any_cast(parsedArgs["fa_image"]); std::string md_file = ""; if (parsedArgs.count("md_image")) md_file = us::any_cast(parsedArgs["md_image"]); std::string mask_file = ""; if (parsedArgs.count("mask")) mask_file = us::any_cast(parsedArgs["mask"]); if (no_relax && no_diff) { MITK_INFO << "Incompatible options. Nothing to optimize."; return EXIT_FAILURE; } itk::Image< unsigned char,3 >::Pointer mask = nullptr; if (mask_file.compare("")!=0) { mitk::Image::Pointer mitk_mask = dynamic_cast(mitk::IOUtil::Load(mask_file)[0].GetPointer()); mitk::CastToItkImage(mitk_mask, mask); } std::vector< double > histogram_modifiers; itk::Image< double,3 >::Pointer fa_image = nullptr; if (fa_file.compare("")!=0) { mitk::Image::Pointer mitk_img = dynamic_cast(mitk::IOUtil::Load(fa_file)[0].GetPointer()); mitk::CastToItkImage(mitk_img, fa_image); - int binsPerDimension = 10; + int binsPerDimension = 20; using ImageToHistogramFilterType = itk::Statistics::MaskedImageToHistogramFilter< itk::Image< double,3 >, itk::Image< unsigned char,3 > >; ImageToHistogramFilterType::HistogramType::MeasurementVectorType lowerBound(binsPerDimension); lowerBound.Fill(0.0); ImageToHistogramFilterType::HistogramType::MeasurementVectorType upperBound(binsPerDimension); upperBound.Fill(1.0); ImageToHistogramFilterType::HistogramType::SizeType size(1); size.Fill(binsPerDimension); ImageToHistogramFilterType::Pointer imageToHistogramFilter = ImageToHistogramFilterType::New(); imageToHistogramFilter->SetInput( fa_image ); imageToHistogramFilter->SetHistogramBinMinimum( lowerBound ); imageToHistogramFilter->SetHistogramBinMaximum( upperBound ); imageToHistogramFilter->SetHistogramSize( size ); imageToHistogramFilter->SetMaskImage(mask); imageToHistogramFilter->SetMaskValue(1); imageToHistogramFilter->Update(); ImageToHistogramFilterType::HistogramType* histogram = imageToHistogramFilter->GetOutput(); unsigned int max = 0; for(unsigned int i = 0; i < histogram->GetSize()[0]; ++i) { if (histogram->GetFrequency(i)>max) max = histogram->GetFrequency(i); } for(unsigned int i = 0; i < histogram->GetSize()[0]; ++i) { histogram_modifiers.push_back((double)max/histogram->GetFrequency(i)); MITK_INFO << histogram_modifiers.back(); } } itk::Image< double,3 >::Pointer md_image = nullptr; if (md_file.compare("")!=0) { mitk::Image::Pointer mitk_img = dynamic_cast(mitk::IOUtil::Load(md_file)[0].GetPointer()); mitk::CastToItkImage(mitk_img, md_image); } FiberfoxParameters parameters; parameters.LoadParameters(paramName); MITK_INFO << "Loading target image"; typedef itk::VectorImage< short, 3 > ItkDwiType; mitk::PreferenceListReaderOptionsFunctor functor = mitk::PreferenceListReaderOptionsFunctor({"Diffusion Weighted Images", "Fiberbundles"}, {}); mitk::Image::Pointer dwi = dynamic_cast(mitk::IOUtil::Load(us::any_cast(parsedArgs["target"]), &functor)[0].GetPointer()); ItkDwiType::Pointer reference = mitk::DiffusionPropertyHelper::GetItkVectorImage(dwi); parameters.m_SignalGen.m_ImageRegion = reference->GetLargestPossibleRegion(); parameters.m_SignalGen.m_ImageSpacing = reference->GetSpacing(); parameters.m_SignalGen.m_ImageOrigin = reference->GetOrigin(); parameters.m_SignalGen.m_ImageDirection = reference->GetDirection(); parameters.SetBvalue(static_cast(dwi->GetProperty(mitk::DiffusionPropertyHelper::REFERENCEBVALUEPROPERTYNAME.c_str()).GetPointer() )->GetValue()); parameters.SetGradienDirections(static_cast( dwi->GetProperty(mitk::DiffusionPropertyHelper::GRADIENTCONTAINERPROPERTYNAME.c_str()).GetPointer() )->GetGradientDirectionsContainer()); mitk::BaseData::Pointer inputData = mitk::IOUtil::Load(input, &functor)[0]; itk::TractsToDWIImageFilter< short >::Pointer tractsToDwiFilter = itk::TractsToDWIImageFilter< short >::New(); tractsToDwiFilter->SetFiberBundle(dynamic_cast(inputData.GetPointer())); tractsToDwiFilter->SetParameters(parameters); tractsToDwiFilter->Update(); ItkDwiType::Pointer sim = tractsToDwiFilter->GetOutput(); { mitk::Image::Pointer image = mitk::GrabItkImageMemory( tractsToDwiFilter->GetOutput() ); image->GetPropertyList()->ReplaceProperty( mitk::DiffusionPropertyHelper::GRADIENTCONTAINERPROPERTYNAME.c_str(), mitk::GradientDirectionsProperty::New( parameters.m_SignalGen.GetGradientDirections() ) ); image->GetPropertyList()->ReplaceProperty( mitk::DiffusionPropertyHelper::REFERENCEBVALUEPROPERTYNAME.c_str(), mitk::FloatProperty::New( parameters.m_SignalGen.GetBvalue() ) ); mitk::DiffusionPropertyHelper propertyHelper( image ); propertyHelper.InitializeImage(); mitk::IOUtil::Save(image, "initial.dwi"); } MITK_INFO << "\n\n"; MITK_INFO << "Iterations: " << iterations; MITK_INFO << "start_temp: " << start_temp; MITK_INFO << "end_temp: " << end_temp; double alpha = log(end_temp/start_temp); int accepted = 0; double last_error = 9999999; if (fa_image.IsNotNull()) { MITK_INFO << "Calculating FA error"; last_error = CalcErrorFA(histogram_modifiers, dwi, sim, mask, fa_image, md_image); } else { MITK_INFO << "Calculating raw-image error"; last_error = CalcErrorSignal(reference, sim, mask); } MITK_INFO << "Initial E = " << last_error; MITK_INFO << "\n\n**************************************************************************************"; for (int i=0; i uint1(0, 1); FiberfoxParameters proposal(parameters); int select = uint1(randgen); if (no_relax) select = 0; else if (no_diff) select = 1; if (select==0) proposal = MakeProposalDiff(proposal, temperature); else proposal = MakeProposalRelaxation(proposal, temperature); std::streambuf *old = cout.rdbuf(); // <-- save std::stringstream ss; std::cout.rdbuf (ss.rdbuf()); itk::TractsToDWIImageFilter< short >::Pointer tractsToDwiFilter = itk::TractsToDWIImageFilter< short >::New(); tractsToDwiFilter->SetFiberBundle(dynamic_cast(inputData.GetPointer())); tractsToDwiFilter->SetParameters(proposal); tractsToDwiFilter->Update(); ItkDwiType::Pointer sim = tractsToDwiFilter->GetOutput(); std::cout.rdbuf (old); // <-- restore double new_error = 9999999; if (fa_image.IsNotNull()) new_error = CalcErrorFA(histogram_modifiers, dwi, sim, mask, fa_image, md_image); else new_error = CalcErrorSignal(reference, sim, mask); MITK_INFO << "E = " << new_error << "(" << new_error-last_error << ")"; if (last_errorGetOutput() ); image->GetPropertyList()->ReplaceProperty( mitk::DiffusionPropertyHelper::GRADIENTCONTAINERPROPERTYNAME.c_str(), mitk::GradientDirectionsProperty::New( parameters.m_SignalGen.GetGradientDirections() ) ); image->GetPropertyList()->ReplaceProperty( mitk::DiffusionPropertyHelper::REFERENCEBVALUEPROPERTYNAME.c_str(), mitk::FloatProperty::New( parameters.m_SignalGen.GetBvalue() ) ); mitk::DiffusionPropertyHelper propertyHelper( image ); propertyHelper.InitializeImage(); mitk::IOUtil::Save(image, "optimized.dwi"); proposal.SaveParameters("optimized.ffp"); std::cout.rdbuf (old); // <-- restore accepted++; MITK_INFO << "Accepted (acc. rate " << (float)accepted/(i+1) << ")"; parameters = FiberfoxParameters(proposal); last_error = new_error; } MITK_INFO << "\n\n\n"; } return EXIT_SUCCESS; } diff --git a/Modules/DiffusionImaging/FiberTracking/cmdapps/Tractography/StreamlineTractography.cpp b/Modules/DiffusionImaging/FiberTracking/cmdapps/Tractography/StreamlineTractography.cpp index 9968c4979c..425e5e764b 100755 --- a/Modules/DiffusionImaging/FiberTracking/cmdapps/Tractography/StreamlineTractography.cpp +++ b/Modules/DiffusionImaging/FiberTracking/cmdapps/Tractography/StreamlineTractography.cpp @@ -1,520 +1,574 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include +#include #define _USE_MATH_DEFINES #include const int numOdfSamples = 200; typedef itk::Image< itk::Vector< float, numOdfSamples > , 3 > SampledShImageType; /*! \brief */ int main(int argc, char* argv[]) { mitkCommandLineParser parser; parser.setTitle("Streamline Tractography"); parser.setCategory("Fiber Tracking and Processing Methods"); parser.setDescription("Perform streamline tractography"); parser.setContributor("MIC"); // parameters fo all methods parser.setArgumentPrefix("--", "-"); parser.beginGroup("1. Mandatory arguments:"); parser.addArgument("input", "i", mitkCommandLineParser::StringList, "Input:", "input image (multiple possible for 'DetTensor' algorithm)", us::Any(), false); parser.addArgument("algorithm", "a", mitkCommandLineParser::String, "Algorithm:", "which algorithm to use (Peaks, DetTensor, ProbTensor, DetODF, ProbODF, DetRF, ProbRF)", us::Any(), false); parser.addArgument("out", "o", mitkCommandLineParser::OutputDirectory, "Output:", "output fiberbundle/probability map", us::Any(), false); parser.endGroup(); parser.beginGroup("2. Seeding:"); parser.addArgument("seeds", "", mitkCommandLineParser::Int, "Seeds per voxel:", "number of seed points per voxel", 1); parser.addArgument("seed_image", "", mitkCommandLineParser::String, "Seed image:", "mask image defining seed voxels", us::Any()); parser.addArgument("trials_per_seed", "", mitkCommandLineParser::Int, "Max. trials per seed:", "try each seed N times until a valid streamline is obtained (only for probabilistic tractography)", 10); parser.addArgument("max_tracts", "", mitkCommandLineParser::Int, "Max. number of tracts:", "tractography is stopped if the reconstructed number of tracts is exceeded", -1); parser.endGroup(); parser.beginGroup("3. Tractography constraints:"); parser.addArgument("tracking_mask", "", mitkCommandLineParser::String, "Mask image:", "streamlines leaving the mask will stop immediately", us::Any()); parser.addArgument("stop_image", "", mitkCommandLineParser::String, "Stop ROI image:", "streamlines entering the mask will stop immediately", us::Any()); parser.addArgument("exclusion_image", "", mitkCommandLineParser::String, "Exclusion ROI image:", "streamlines entering the mask will be discarded", us::Any()); parser.addArgument("ep_constraint", "", mitkCommandLineParser::String, "Endpoint constraint:", "determines which fibers are accepted based on their endpoint location - options are NONE, EPS_IN_TARGET, EPS_IN_TARGET_LABELDIFF, EPS_IN_SEED_AND_TARGET, MIN_ONE_EP_IN_TARGET, ONE_EP_IN_TARGET and NO_EP_IN_TARGET", us::Any()); parser.addArgument("target_image", "", mitkCommandLineParser::String, "Target ROI image:", "effact depends on the chosen endpoint constraint (option ep_constraint)", us::Any()); parser.endGroup(); parser.beginGroup("4. Streamline integration parameters:"); parser.addArgument("sharpen_odfs", "", mitkCommandLineParser::Bool, "SHarpen ODFs:", "if you are using dODF images as input, it is advisable to sharpen the ODFs (min-max normalize and raise to the power of 4). this is not necessary for CSD fODFs, since they are narurally much sharper."); parser.addArgument("cutoff", "", mitkCommandLineParser::Float, "Cutoff:", "set the FA, GFA or Peak amplitude cutoff for terminating tracks", 0.1); parser.addArgument("odf_cutoff", "", mitkCommandLineParser::Float, "ODF Cutoff:", "threshold on the ODF magnitude. this is useful in case of CSD fODF tractography.", 0.0); parser.addArgument("step_size", "", mitkCommandLineParser::Float, "Step size:", "step size (in voxels)", 0.5); parser.addArgument("min_tract_length", "", mitkCommandLineParser::Float, "Min. tract length:", "minimum fiber length (in mm)", 20); parser.addArgument("angular_threshold", "", mitkCommandLineParser::Float, "Angular threshold:", "angular threshold between two successive steps, (default: 90° * step_size, minimum 15°)"); parser.addArgument("loop_check", "", mitkCommandLineParser::Float, "Check for loops:", "threshold on angular stdev over the last 4 voxel lengths"); parser.endGroup(); - parser.beginGroup("5. Neighborhood sampling:"); + parser.beginGroup("5. Tractography prior:"); + parser.addArgument("prior_image", "", mitkCommandLineParser::String, "Peak prior:", "tractography prior in thr for of a peak image", us::Any()); + parser.addArgument("prior_weight", "", mitkCommandLineParser::Float, "Prior weight", "weighting factor between prior and data.", 0.5); + parser.addArgument("restrict_to_prior", "", mitkCommandLineParser::Bool, "Restrict to prior:", "restrict tractography to regions where the prior is valid."); + parser.addArgument("new_directions_from_prior", "", mitkCommandLineParser::Bool, "New directios from prior:", "the prior can create directions where there are none in the data."); + parser.endGroup(); + + parser.beginGroup("6. Neighborhood sampling:"); parser.addArgument("num_samples", "", mitkCommandLineParser::Int, "Num. neighborhood samples:", "number of neighborhood samples that are use to determine the next progression direction", 0); parser.addArgument("sampling_distance", "", mitkCommandLineParser::Float, "Sampling distance:", "distance of neighborhood sampling points (in voxels)", 0.25); parser.addArgument("use_stop_votes", "", mitkCommandLineParser::Bool, "Use stop votes:", "use stop votes"); parser.addArgument("use_only_forward_samples", "", mitkCommandLineParser::Bool, "Use only forward samples:", "use only forward samples"); parser.endGroup(); - parser.beginGroup("6. Tensor tractography specific:"); + parser.beginGroup("7. Tensor tractography specific:"); parser.addArgument("tend_f", "", mitkCommandLineParser::Float, "Weight f", "weighting factor between first eigenvector (f=1 equals FACT tracking) and input vector dependent direction (f=0).", 1.0); parser.addArgument("tend_g", "", mitkCommandLineParser::Float, "Weight g", "weighting factor between input vector (g=0) and tensor deflection (g=1 equals TEND tracking)", 0.0); parser.endGroup(); - parser.beginGroup("7. Random forest tractography specific:"); + parser.beginGroup("8. Random forest tractography specific:"); parser.addArgument("forest", "", mitkCommandLineParser::String, "Forest:", "input random forest (HDF5 file)", us::Any()); parser.addArgument("use_sh_features", "", mitkCommandLineParser::Bool, "Use SH features:", "use SH features"); parser.endGroup(); - parser.beginGroup("8. Additional input:"); + parser.beginGroup("9. Additional input:"); parser.addArgument("additional_images", "", mitkCommandLineParser::StringList, "Additional images:", "specify a list of float images that hold additional information (FA, GFA, additional features for RF tractography)", us::Any()); parser.endGroup(); - parser.beginGroup("9. Misc:"); + parser.beginGroup("10. Misc:"); parser.addArgument("flip_x", "", mitkCommandLineParser::Bool, "Flip X:", "multiply x-coordinate of direction proposal by -1"); parser.addArgument("flip_y", "", mitkCommandLineParser::Bool, "Flip Y:", "multiply y-coordinate of direction proposal by -1"); parser.addArgument("flip_z", "", mitkCommandLineParser::Bool, "Flip Z:", "multiply z-coordinate of direction proposal by -1"); parser.addArgument("no_data_interpolation", "", mitkCommandLineParser::Bool, "Don't interpolate input data:", "don't interpolate input image values"); parser.addArgument("no_mask_interpolation", "", mitkCommandLineParser::Bool, "Don't interpolate masks:", "don't interpolate mask image values"); parser.addArgument("compress", "", mitkCommandLineParser::Float, "Compress:", "compress output fibers using the given error threshold (in mm)"); parser.endGroup(); std::map parsedArgs = parser.parseArguments(argc, argv); if (parsedArgs.size()==0) return EXIT_FAILURE; mitkCommandLineParser::StringContainerType input_files = us::any_cast(parsedArgs["input"]); std::string outFile = us::any_cast(parsedArgs["out"]); std::string algorithm = us::any_cast(parsedArgs["algorithm"]); + std::string prior_image = ""; + if (parsedArgs.count("prior_image")) + prior_image = us::any_cast(parsedArgs["prior_image"]); + + float prior_weight = 0.5; + if (parsedArgs.count("prior_weight")) + prior_weight = us::any_cast(parsedArgs["prior_weight"]); + + bool restrict_to_prior = false; + if (parsedArgs.count("restrict_to_prior")) + restrict_to_prior = us::any_cast(parsedArgs["restrict_to_prior"]); + + bool new_directions_from_prior = false; + if (parsedArgs.count("new_directions_from_prior")) + new_directions_from_prior = us::any_cast(parsedArgs["new_directions_from_prior"]); + bool sharpen_odfs = false; if (parsedArgs.count("sharpen_odfs")) sharpen_odfs = us::any_cast(parsedArgs["sharpen_odfs"]); bool interpolate = true; if (parsedArgs.count("no_data_interpolation")) interpolate = !us::any_cast(parsedArgs["no_data_interpolation"]); bool mask_interpolation = true; if (parsedArgs.count("no_mask_interpolation")) interpolate = !us::any_cast(parsedArgs["no_mask_interpolation"]); bool use_sh_features = false; if (parsedArgs.count("use_sh_features")) use_sh_features = us::any_cast(parsedArgs["use_sh_features"]); bool use_stop_votes = false; if (parsedArgs.count("use_stop_votes")) use_stop_votes = us::any_cast(parsedArgs["use_stop_votes"]); bool use_only_forward_samples = false; if (parsedArgs.count("use_only_forward_samples")) use_only_forward_samples = us::any_cast(parsedArgs["use_only_forward_samples"]); bool flip_x = false; if (parsedArgs.count("flip_x")) flip_x = us::any_cast(parsedArgs["flip_x"]); bool flip_y = false; if (parsedArgs.count("flip_y")) flip_y = us::any_cast(parsedArgs["flip_y"]); bool flip_z = false; if (parsedArgs.count("flip_z")) flip_z = us::any_cast(parsedArgs["flip_z"]); bool apply_image_rotation = false; if (parsedArgs.count("apply_image_rotation")) apply_image_rotation = us::any_cast(parsedArgs["apply_image_rotation"]); float compress = -1; if (parsedArgs.count("compress")) compress = us::any_cast(parsedArgs["compress"]); float min_tract_length = 20; if (parsedArgs.count("min_tract_length")) min_tract_length = us::any_cast(parsedArgs["min_tract_length"]); float loop_check = -1; if (parsedArgs.count("loop_check")) loop_check = us::any_cast(parsedArgs["loop_check"]); std::string forestFile; if (parsedArgs.count("forest")) forestFile = us::any_cast(parsedArgs["forest"]); std::string maskFile = ""; if (parsedArgs.count("tracking_mask")) maskFile = us::any_cast(parsedArgs["tracking_mask"]); std::string seedFile = ""; if (parsedArgs.count("seed_image")) seedFile = us::any_cast(parsedArgs["seed_image"]); std::string targetFile = ""; if (parsedArgs.count("target_image")) targetFile = us::any_cast(parsedArgs["target_image"]); std::string exclusionFile = ""; if (parsedArgs.count("exclusion_image")) exclusionFile = us::any_cast(parsedArgs["exclusion_image"]); std::string stopFile = ""; if (parsedArgs.count("stop_image")) stopFile = us::any_cast(parsedArgs["stop_image"]); std::string ep_constraint = "NONE"; if (parsedArgs.count("ep_constraint")) ep_constraint = us::any_cast(parsedArgs["ep_constraint"]); float cutoff = 0.1; if (parsedArgs.count("cutoff")) cutoff = us::any_cast(parsedArgs["cutoff"]); float odf_cutoff = 0.0; if (parsedArgs.count("odf_cutoff")) odf_cutoff = us::any_cast(parsedArgs["odf_cutoff"]); float stepsize = -1; if (parsedArgs.count("step_size")) stepsize = us::any_cast(parsedArgs["step_size"]); float sampling_distance = -1; if (parsedArgs.count("sampling_distance")) sampling_distance = us::any_cast(parsedArgs["sampling_distance"]); int num_samples = 0; if (parsedArgs.count("num_samples")) num_samples = us::any_cast(parsedArgs["num_samples"]); int num_seeds = 1; if (parsedArgs.count("seeds")) num_seeds = us::any_cast(parsedArgs["seeds"]); unsigned int trials_per_seed = 10; if (parsedArgs.count("trials_per_seed")) trials_per_seed = us::any_cast(parsedArgs["trials_per_seed"]); float tend_f = 1; if (parsedArgs.count("tend_f")) tend_f = us::any_cast(parsedArgs["tend_f"]); float tend_g = 0; if (parsedArgs.count("tend_g")) tend_g = us::any_cast(parsedArgs["tend_g"]); float angular_threshold = -1; if (parsedArgs.count("angular_threshold")) angular_threshold = us::any_cast(parsedArgs["angular_threshold"]); unsigned int max_tracts = -1; if (parsedArgs.count("max_tracts")) max_tracts = us::any_cast(parsedArgs["max_tracts"]); std::string ext = itksys::SystemTools::GetFilenameExtension(outFile); if (ext != ".fib" && ext != ".trk") { MITK_INFO << "Output file format not supported. Use one of .fib, .trk, .nii, .nii.gz, .nrrd"; return EXIT_FAILURE; } // LOAD DATASETS mitkCommandLineParser::StringContainerType addFiles; if (parsedArgs.count("additional_images")) addFiles = us::any_cast(parsedArgs["additional_images"]); typedef itk::Image ItkFloatImgType; MITK_INFO << "loading input"; std::vector< mitk::Image::Pointer > input_images; for (unsigned int i=0; i(mitk::IOUtil::Load(input_files.at(i))[0].GetPointer()); input_images.push_back(mitkImage); } ItkFloatImgType::Pointer mask = nullptr; if (!maskFile.empty()) { MITK_INFO << "loading mask image"; mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(maskFile)[0].GetPointer()); mask = ItkFloatImgType::New(); mitk::CastToItkImage(img, mask); } ItkFloatImgType::Pointer seed = nullptr; if (!seedFile.empty()) { MITK_INFO << "loading seed ROI image"; mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(seedFile)[0].GetPointer()); seed = ItkFloatImgType::New(); mitk::CastToItkImage(img, seed); } ItkFloatImgType::Pointer stop = nullptr; if (!stopFile.empty()) { MITK_INFO << "loading stop ROI image"; mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(stopFile)[0].GetPointer()); stop = ItkFloatImgType::New(); mitk::CastToItkImage(img, stop); } ItkFloatImgType::Pointer target = nullptr; if (!targetFile.empty()) { MITK_INFO << "loading target ROI image"; mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(targetFile)[0].GetPointer()); target = ItkFloatImgType::New(); mitk::CastToItkImage(img, target); } ItkFloatImgType::Pointer exclusion = nullptr; if (!exclusionFile.empty()) { MITK_INFO << "loading exclusion ROI image"; mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(exclusionFile)[0].GetPointer()); exclusion = ItkFloatImgType::New(); mitk::CastToItkImage(img, exclusion); } MITK_INFO << "loading additional images"; std::vector< std::vector< ItkFloatImgType::Pointer > > addImages; addImages.push_back(std::vector< ItkFloatImgType::Pointer >()); for (auto file : addFiles) { mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(file)[0].GetPointer()); ItkFloatImgType::Pointer itkimg = ItkFloatImgType::New(); mitk::CastToItkImage(img, itkimg); addImages.at(0).push_back(itkimg); } // ////////////////////////////////////////////////////////////////// // omp_set_num_threads(1); if (algorithm == "ProbTensor") { typedef mitk::ImageToItk< mitk::TrackingHandlerTensor::ItkTensorImageType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(input_images.at(0)); caster->Update(); mitk::TrackingHandlerTensor::ItkTensorImageType::Pointer itkTensorImg = caster->GetOutput(); typedef itk::TensorImageToOdfImageFilter< float, float > FilterType; FilterType::Pointer filter = FilterType::New(); filter->SetInput( itkTensorImg ); filter->Update(); mitk::Image::Pointer image = mitk::Image::New(); FilterType::OutputImageType::Pointer outimg = filter->GetOutput(); image->InitializeByItk( outimg.GetPointer() ); image->SetVolume( outimg->GetBufferPointer() ); input_images.clear(); input_images.push_back(image); sharpen_odfs = true; odf_cutoff = 0; } typedef itk::StreamlineTrackingFilter TrackerType; TrackerType::Pointer tracker = TrackerType::New(); + if (!prior_image.empty()) + { + mitk::PreferenceListReaderOptionsFunctor functor = mitk::PreferenceListReaderOptionsFunctor({"Peak Image"}, {}); + mitk::PeakImage::Pointer priorImage = dynamic_cast(mitk::IOUtil::Load(prior_image, &functor)[0].GetPointer()); + + if (priorImage.IsNull()) + { + MITK_INFO << "Only peak images are supported as prior at the moment!"; + return EXIT_FAILURE; + } + + mitk::TrackingDataHandler* priorhandler = new mitk::TrackingHandlerPeaks(); + + typedef mitk::ImageToItk< mitk::TrackingHandlerPeaks::PeakImgType > CasterType; + CasterType::Pointer caster = CasterType::New(); + caster->SetInput(priorImage); + caster->Update(); + mitk::TrackingHandlerPeaks::PeakImgType::Pointer itkImg = caster->GetOutput(); + + dynamic_cast(priorhandler)->SetPeakImage(itkImg); + dynamic_cast(priorhandler)->SetPeakThreshold(0.0); + dynamic_cast(priorhandler)->SetInterpolate(interpolate); + dynamic_cast(priorhandler)->SetMode(mitk::TrackingDataHandler::MODE::DETERMINISTIC); + + tracker->SetTrackingPriorHandler(priorhandler); + tracker->SetTrackingPriorWeight(prior_weight); + tracker->SetTrackingPriorAsMask(restrict_to_prior); + tracker->SetIntroduceDirectionsFromPrior(new_directions_from_prior); + } + mitk::TrackingDataHandler* handler; if (algorithm == "DetRF" || algorithm == "ProbRF") { mitk::TractographyForest::Pointer forest = dynamic_cast(mitk::IOUtil::Load(forestFile)[0].GetPointer()); if (forest.IsNull()) mitkThrow() << "Forest file " << forestFile << " could not be read."; if (use_sh_features) { handler = new mitk::TrackingHandlerRandomForest<6,28>(); dynamic_cast*>(handler)->SetForest(forest); dynamic_cast*>(handler)->AddDwi(input_images.at(0)); dynamic_cast*>(handler)->SetAdditionalFeatureImages(addImages); } else { handler = new mitk::TrackingHandlerRandomForest<6,100>(); dynamic_cast*>(handler)->SetForest(forest); dynamic_cast*>(handler)->AddDwi(input_images.at(0)); dynamic_cast*>(handler)->SetAdditionalFeatureImages(addImages); } if (algorithm == "ProbRF") handler->SetMode(mitk::TrackingDataHandler::MODE::PROBABILISTIC); } else if (algorithm == "Peaks") { handler = new mitk::TrackingHandlerPeaks(); typedef mitk::ImageToItk< mitk::TrackingHandlerPeaks::PeakImgType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(input_images.at(0)); caster->Update(); mitk::TrackingHandlerPeaks::PeakImgType::Pointer itkImg = caster->GetOutput(); dynamic_cast(handler)->SetPeakImage(itkImg); dynamic_cast(handler)->SetApplyDirectionMatrix(apply_image_rotation); dynamic_cast(handler)->SetPeakThreshold(cutoff); } else if (algorithm == "DetTensor") { handler = new mitk::TrackingHandlerTensor(); for (auto input_image : input_images) { typedef mitk::ImageToItk< mitk::TrackingHandlerTensor::ItkTensorImageType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(input_image); caster->Update(); mitk::TrackingHandlerTensor::ItkTensorImageType::ConstPointer itkImg = caster->GetOutput(); dynamic_cast(handler)->AddTensorImage(itkImg); } dynamic_cast(handler)->SetFaThreshold(cutoff); dynamic_cast(handler)->SetF(tend_f); dynamic_cast(handler)->SetG(tend_g); if (addImages.at(0).size()>0) dynamic_cast(handler)->SetFaImage(addImages.at(0).at(0)); } else if (algorithm == "DetODF" || algorithm == "ProbODF" || algorithm == "ProbTensor") { handler = new mitk::TrackingHandlerOdf(); typedef mitk::ImageToItk< mitk::TrackingHandlerOdf::ItkOdfImageType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(input_images.at(0)); caster->Update(); mitk::TrackingHandlerOdf::ItkOdfImageType::Pointer itkImg = caster->GetOutput(); dynamic_cast(handler)->SetOdfImage(itkImg); dynamic_cast(handler)->SetGfaThreshold(cutoff); dynamic_cast(handler)->SetOdfThreshold(odf_cutoff); dynamic_cast(handler)->SetSharpenOdfs(sharpen_odfs); if (algorithm == "ProbODF" || algorithm == "ProbTensor") dynamic_cast(handler)->SetMode(mitk::TrackingHandlerOdf::MODE::PROBABILISTIC); if (algorithm == "ProbTensor") dynamic_cast(handler)->SetIsOdfFromTensor(true); if (addImages.at(0).size()>0) dynamic_cast(handler)->SetGfaImage(addImages.at(0).at(0)); } else { MITK_INFO << "Unknown tractography algorithm (" + algorithm+"). Known types are Peaks, DetTensor, ProbTensor, DetODF, ProbODF, DetRF, ProbRF."; return EXIT_FAILURE; } handler->SetInterpolate(interpolate); handler->SetFlipX(flip_x); handler->SetFlipY(flip_y); handler->SetFlipZ(flip_z); if (ep_constraint=="NONE") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::NONE); else if (ep_constraint=="EPS_IN_TARGET") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET); else if (ep_constraint=="EPS_IN_TARGET_LABELDIFF") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET_LABELDIFF); else if (ep_constraint=="EPS_IN_SEED_AND_TARGET") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_SEED_AND_TARGET); else if (ep_constraint=="MIN_ONE_EP_IN_TARGET") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::MIN_ONE_EP_IN_TARGET); else if (ep_constraint=="ONE_EP_IN_TARGET") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::ONE_EP_IN_TARGET); else if (ep_constraint=="NO_EP_IN_TARGET") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::NO_EP_IN_TARGET); MITK_INFO << "Tractography algorithm: " << algorithm; tracker->SetInterpolateMasks(mask_interpolation); tracker->SetNumberOfSamples(num_samples); tracker->SetAngularThreshold(angular_threshold); tracker->SetMaskImage(mask); tracker->SetSeedImage(seed); tracker->SetStoppingRegions(stop); tracker->SetTargetRegions(target); tracker->SetExclusionRegions(exclusion); tracker->SetSeedsPerVoxel(num_seeds); tracker->SetStepSize(stepsize); tracker->SetSamplingDistance(sampling_distance); tracker->SetUseStopVotes(use_stop_votes); tracker->SetOnlyForwardSamples(use_only_forward_samples); tracker->SetLoopCheck(loop_check); tracker->SetMaxNumTracts(max_tracts); tracker->SetTrialsPerSeed(trials_per_seed); tracker->SetTrackingHandler(handler); if (ext != ".fib" && ext != ".trk") tracker->SetUseOutputProbabilityMap(true); tracker->SetMinTractLength(min_tract_length); tracker->Update(); if (ext == ".fib" || ext == ".trk") { vtkSmartPointer< vtkPolyData > poly = tracker->GetFiberPolyData(); mitk::FiberBundle::Pointer outFib = mitk::FiberBundle::New(poly); if (compress > 0) outFib->Compress(compress); mitk::IOUtil::Save(outFib, outFile); } else { TrackerType::ItkDoubleImgType::Pointer outImg = tracker->GetOutputProbabilityMap(); mitk::Image::Pointer img = mitk::Image::New(); img->InitializeByItk(outImg.GetPointer()); img->SetVolume(outImg->GetBufferPointer()); if (ext != ".nii" && ext != ".nii.gz" && ext != ".nrrd") outFile += ".nii.gz"; mitk::IOUtil::Save(img, outFile); } delete handler; return EXIT_SUCCESS; } diff --git a/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/AnchorBasedScoring.cpp b/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/AnchorBasedScoring.cpp index bbae05da57..a5a42ddcec 100755 --- a/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/AnchorBasedScoring.cpp +++ b/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/AnchorBasedScoring.cpp @@ -1,471 +1,473 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include typedef itksys::SystemTools ist; typedef itk::Point PointType4; typedef itk::Image< float, 4 > PeakImgType; typedef itk::Image< unsigned char, 3 > ItkUcharImageType; std::vector< mitk::FiberBundle::Pointer > CombineTractograms(std::vector< mitk::FiberBundle::Pointer > reference, std::vector< mitk::FiberBundle::Pointer > candidates, int skip=-1) { std::vector< mitk::FiberBundle::Pointer > fib; for (auto f : reference) fib.push_back(f); int c = 0; for (auto f : candidates) { if (c!=skip) fib.push_back(f); ++c; } return fib; } std::vector< std::string > get_file_list(const std::string& path, std::vector< std::string > extensions={".fib", ".trk"}) { std::vector< std::string > file_list; itk::Directory::Pointer dir = itk::Directory::New(); if (dir->Load(path.c_str())) { int n = dir->GetNumberOfFiles(); for (int r = 0; r < n; r++) { const char *filename = dir->GetFile(r); std::string ext = ist::GetFilenameExtension(filename); for (auto e : extensions) { if (ext==e) { file_list.push_back(path + '/' + filename); break; } } } } return file_list; } /*! \brief Fits the tractogram to the input peak image by assigning a weight to each fiber (similar to https://doi.org/10.1016/j.neuroimage.2015.06.092). */ int main(int argc, char* argv[]) { mitkCommandLineParser parser; parser.setTitle("Anchor Based Scoring"); parser.setCategory("Fiber Tracking Evaluation"); parser.setDescription(""); parser.setContributor("MIC"); parser.setArgumentPrefix("--", "-"); parser.addArgument("", "a", mitkCommandLineParser::InputFile, "Anchor tractogram:", "anchor tracts in one tractogram file", us::Any(), false); parser.addArgument("", "p", mitkCommandLineParser::InputFile, "Input peaks:", "input peak image", us::Any(), false); parser.addArgument("", "c", mitkCommandLineParser::InputDirectory, "Candidates folder:", "folder containing candidate tracts", us::Any(), false); parser.addArgument("", "o", mitkCommandLineParser::OutputDirectory, "Output folder:", "output folder", us::Any(), false); parser.addArgument("anchor_masks", "", mitkCommandLineParser::StringList, "Reference Masks:", "reference tract masks for accuracy evaluation"); parser.addArgument("mask", "", mitkCommandLineParser::InputFile, "Mask image:", "scoring is only performed inside the mask image"); parser.addArgument("greedy_add", "", mitkCommandLineParser::Bool, "Greedy:", "if enabled, the candidate tracts are not jointly fitted to the residual image but one after the other employing a greedy scheme", false); parser.addArgument("lambda", "", mitkCommandLineParser::Float, "Lambda:", "modifier for regularization", 0.1); - parser.addArgument("dont_filter_outliers", "", mitkCommandLineParser::Bool, "Don't filter outliers:", "don't perform second optimization run with an upper weight bound based on the first weight estimation (95% quantile)", false); - parser.addArgument("regu", "", mitkCommandLineParser::String, "Regularization:", "MSM, MSE, LocalMSE, GroupLasso, GroupMSE, NONE (default)"); + parser.addArgument("filter_outliers", "", mitkCommandLineParser::Bool, "Filter outliers:", "perform second optimization run with an upper weight bound based on the first weight estimation (99% quantile)", false); + parser.addArgument("regu", "", mitkCommandLineParser::String, "Regularization:", "MSM, Variance, VoxelVariance, Lasso, GroupLasso, GroupVariance, NONE (default)"); std::map parsedArgs = parser.parseArguments(argc, argv); if (parsedArgs.size()==0) return EXIT_FAILURE; std::string anchors_file = us::any_cast(parsedArgs["a"]); std::string peak_file_name = us::any_cast(parsedArgs["p"]); std::string candidate_tract_folder = us::any_cast(parsedArgs["c"]); std::string out_folder = us::any_cast(parsedArgs["o"]); bool greedy_add = false; if (parsedArgs.count("greedy_add")) greedy_add = us::any_cast(parsedArgs["greedy_add"]); float lambda = 0.1; if (parsedArgs.count("lambda")) lambda = us::any_cast(parsedArgs["lambda"]); - bool filter_outliers = true; - if (parsedArgs.count("dont_filter_outliers")) - filter_outliers = !us::any_cast(parsedArgs["dont_filter_outliers"]); + bool filter_outliers = false; + if (parsedArgs.count("filter_outliers")) + filter_outliers = us::any_cast(parsedArgs["filter_outliers"]); std::string mask_file = ""; if (parsedArgs.count("mask")) mask_file = us::any_cast(parsedArgs["mask"]); mitkCommandLineParser::StringContainerType anchor_mask_files; if (parsedArgs.count("anchor_masks")) anchor_mask_files = us::any_cast(parsedArgs["anchor_masks"]); std::string regu = "NONE"; if (parsedArgs.count("regu")) regu = us::any_cast(parsedArgs["regu"]); try { itk::TimeProbe clock; clock.Start(); if (!ist::PathExists(out_folder)) { MITK_INFO << "Creating output directory"; ist::MakeDirectory(out_folder); } MITK_INFO << "Loading data"; std::streambuf *old = cout.rdbuf(); // <-- save std::stringstream ss; std::cout.rdbuf (ss.rdbuf()); // <-- redirect ofstream logfile; logfile.open (out_folder + "log.txt"); itk::ImageFileWriter< PeakImgType >::Pointer peak_image_writer = itk::ImageFileWriter< PeakImgType >::New(); mitk::PreferenceListReaderOptionsFunctor functor = mitk::PreferenceListReaderOptionsFunctor({"Peak Image", "Fiberbundles"}, {}); mitk::Image::Pointer inputImage = dynamic_cast(mitk::IOUtil::Load(peak_file_name, &functor)[0].GetPointer()); float minSpacing = 1; if(inputImage->GetGeometry()->GetSpacing()[0]GetGeometry()->GetSpacing()[1] && inputImage->GetGeometry()->GetSpacing()[0]GetGeometry()->GetSpacing()[2]) minSpacing = inputImage->GetGeometry()->GetSpacing()[0]; else if (inputImage->GetGeometry()->GetSpacing()[1] < inputImage->GetGeometry()->GetSpacing()[2]) minSpacing = inputImage->GetGeometry()->GetSpacing()[1]; else minSpacing = inputImage->GetGeometry()->GetSpacing()[2]; // Load mask file. Fit is only performed inside the mask itk::FitFibersToImageFilter::UcharImgType::Pointer mask = nullptr; if (mask_file.compare("")!=0) { mitk::Image::Pointer mitk_mask = dynamic_cast(mitk::IOUtil::Load(mask_file)[0].GetPointer()); mitk::CastToItkImage(mitk_mask, mask); } // Load masks covering the true positives for evaluation purposes std::vector< itk::FitFibersToImageFilter::UcharImgType::Pointer > reference_masks; for (auto filename : anchor_mask_files) { itk::FitFibersToImageFilter::UcharImgType::Pointer ref_mask = nullptr; mitk::Image::Pointer ref_mitk_mask = dynamic_cast(mitk::IOUtil::Load(filename)[0].GetPointer()); mitk::CastToItkImage(ref_mitk_mask, ref_mask); reference_masks.push_back(ref_mask); } // Load peak image typedef mitk::ImageToItk< PeakImgType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(inputImage); caster->Update(); PeakImgType::Pointer peak_image = caster->GetOutput(); // Load all candidate tracts std::vector< std::string > candidate_tract_files = get_file_list(candidate_tract_folder); std::vector< mitk::FiberBundle::Pointer > input_candidates; for (std::string f : candidate_tract_files) { mitk::FiberBundle::Pointer fib = dynamic_cast(mitk::IOUtil::Load(f)[0].GetPointer()); if (fib.IsNull()) continue; if (fib->GetNumFibers()<=0) continue; fib->ResampleLinear(minSpacing/10.0); input_candidates.push_back(fib); } std::cout.rdbuf (old); // <-- restore MITK_INFO << "Loaded " << candidate_tract_files.size() << " candidate tracts."; double rmse = 0.0; int iteration = 0; std::string name = "NOANCHOR"; // Load reference tractogram consisting of all known tracts std::vector< mitk::FiberBundle::Pointer > input_reference; mitk::FiberBundle::Pointer anchor_tractogram = dynamic_cast(mitk::IOUtil::Load(anchors_file)[0].GetPointer()); if ( !(anchor_tractogram.IsNull() || anchor_tractogram->GetNumFibers()==0) ) { std::streambuf *old = cout.rdbuf(); // <-- save std::stringstream ss; std::cout.rdbuf (ss.rdbuf()); // <-- redirect anchor_tractogram->ResampleLinear(minSpacing/10.0); std::cout.rdbuf (old); // <-- restore input_reference.push_back(anchor_tractogram); // Fit known tracts to peak image to obtain underexplained image MITK_INFO << "Fit anchor tracts"; itk::FitFibersToImageFilter::Pointer fitter = itk::FitFibersToImageFilter::New(); fitter->SetTractograms(input_reference); fitter->SetLambda(lambda); fitter->SetFilterOutliers(filter_outliers); fitter->SetPeakImage(peak_image); fitter->SetVerbose(true); fitter->SetResampleFibers(false); fitter->SetMaskImage(mask); fitter->SetRegularization(VnlCostFunction::REGU::NONE); fitter->Update(); rmse = fitter->GetRMSE(); vnl_vector rms_diff = fitter->GetRmsDiffPerBundle(); logfile << "RMS_DIFF: " << setprecision(5) << rms_diff[0] << " " << name << " RMSE: " << rmse << "\n"; name = ist::GetFilenameWithoutExtension(anchors_file); mitk::FiberBundle::Pointer anchor_tracts = fitter->GetTractograms().at(0); anchor_tracts->SetFiberColors(255,255,255); mitk::IOUtil::Save(anchor_tracts, out_folder + boost::lexical_cast((int)(100000*rms_diff[0])) + "_" + name + ".fib"); peak_image = fitter->GetUnderexplainedImage(); peak_image_writer->SetInput(peak_image); peak_image_writer->SetFileName(out_folder + "Residual_" + name + ".nii.gz"); peak_image_writer->Update(); } if (!greedy_add) { MITK_INFO << "Fit candidate tracts"; itk::FitFibersToImageFilter::Pointer fitter = itk::FitFibersToImageFilter::New(); fitter->SetLambda(lambda); fitter->SetFilterOutliers(filter_outliers); fitter->SetVerbose(true); fitter->SetPeakImage(peak_image); fitter->SetResampleFibers(false); fitter->SetMaskImage(mask); fitter->SetTractograms(input_candidates); fitter->SetFitIndividualFibers(true); if (regu=="MSM") fitter->SetRegularization(VnlCostFunction::REGU::MSM); - else if (regu=="MSE") - fitter->SetRegularization(VnlCostFunction::REGU::MSE); - else if (regu=="Local_MSE") - fitter->SetRegularization(VnlCostFunction::REGU::Local_MSE); + else if (regu=="Variance") + fitter->SetRegularization(VnlCostFunction::REGU::VARIANCE); + else if (regu=="Lasso") + fitter->SetRegularization(VnlCostFunction::REGU::LASSO); + else if (regu=="VoxelVariance") + fitter->SetRegularization(VnlCostFunction::REGU::VOXEL_VARIANCE); else if (regu=="GroupLasso") fitter->SetRegularization(VnlCostFunction::REGU::GROUP_LASSO); - else if (regu=="GroupMSE") - fitter->SetRegularization(VnlCostFunction::REGU::GROUP_MSE); + else if (regu=="GroupVariance") + fitter->SetRegularization(VnlCostFunction::REGU::GROUP_VARIANCE); else if (regu=="NONE") fitter->SetRegularization(VnlCostFunction::REGU::NONE); fitter->Update(); vnl_vector rms_diff = fitter->GetRmsDiffPerBundle(); vnl_vector log_rms_diff = rms_diff-rms_diff.min_value() + 1; log_rms_diff = log_rms_diff.apply(std::log); log_rms_diff /= log_rms_diff.max_value(); int c = 0; for (auto fib : input_candidates) { fib->SetFiberWeights( log_rms_diff[c] ); fib->ColorFibersByOrientation(); std::string bundle_name = ist::GetFilenameWithoutExtension(candidate_tract_files.at(c)); std::streambuf *old = cout.rdbuf(); // <-- save std::stringstream ss; std::cout.rdbuf (ss.rdbuf()); // <-- redirect mitk::IOUtil::Save(fib, out_folder + boost::lexical_cast((int)(100000*rms_diff[c])) + "_" + bundle_name + ".fib"); float best_overlap = 0; int best_overlap_index = -1; int m_idx = 0; for (auto ref_mask : reference_masks) { float overlap = fib->GetOverlap(ref_mask, false); if (overlap>best_overlap) { best_overlap = overlap; best_overlap_index = m_idx; } ++m_idx; } unsigned int num_voxels = 0; { itk::TractDensityImageFilter< ItkUcharImageType >::Pointer masks_filter = itk::TractDensityImageFilter< ItkUcharImageType >::New(); masks_filter->SetInputImage(mask); masks_filter->SetBinaryOutput(true); masks_filter->SetFiberBundle(fib); masks_filter->SetUseImageGeometry(true); masks_filter->Update(); num_voxels = masks_filter->GetNumCoveredVoxels(); } double weight_sum = 0; for (int i=0; iGetNumFibers(); i++) weight_sum += fib->GetFiberWeight(i); std::cout.rdbuf (old); // <-- restore logfile << "RMS_DIFF: " << setprecision(5) << rms_diff[c] << " " << bundle_name << " " << num_voxels << " " << fib->GetNumFibers() << " " << weight_sum << "\n"; if (best_overlap_index>=0) logfile << "Best_overlap: " << setprecision(5) << best_overlap << " " << ist::GetFilenameWithoutExtension(anchor_mask_files.at(best_overlap_index)) << "\n"; else logfile << "No_overlap\n"; ++c; } mitk::FiberBundle::Pointer out_fib = mitk::FiberBundle::New(); out_fib = out_fib->AddBundles(input_candidates); out_fib->ColorFibersByFiberWeights(false, true); mitk::IOUtil::Save(out_fib, out_folder + "AllCandidates.fib"); peak_image = fitter->GetUnderexplainedImage(); peak_image_writer->SetInput(peak_image); peak_image_writer->SetFileName(out_folder + "Residual_AllCandidates.nii.gz"); peak_image_writer->Update(); } else { MITK_INFO << "RMSE: " << setprecision(5) << rmse; // fitter->SetPeakImage(peak_image); // Iteratively add candidate bundles in a greedy manner while (!input_candidates.empty()) { double next_rmse = rmse; double num_peaks = 0; mitk::FiberBundle::Pointer best_candidate = nullptr; PeakImgType::Pointer best_candidate_peak_image = nullptr; for (int i=0; i<(int)input_candidates.size(); ++i) { // WHY NECESSARY AGAIN?? itk::FitFibersToImageFilter::Pointer fitter = itk::FitFibersToImageFilter::New(); fitter->SetLambda(lambda); fitter->SetFilterOutliers(filter_outliers); fitter->SetVerbose(false); fitter->SetPeakImage(peak_image); fitter->SetResampleFibers(false); fitter->SetMaskImage(mask); // ****************************** fitter->SetTractograms({input_candidates.at(i)}); std::streambuf *old = cout.rdbuf(); // <-- save std::stringstream ss; std::cout.rdbuf (ss.rdbuf()); // <-- redirect fitter->Update(); std::cout.rdbuf (old); // <-- restore double candidate_rmse = fitter->GetRMSE(); if (candidate_rmseGetNumCoveredDirections(); best_candidate = fitter->GetTractograms().at(0); best_candidate_peak_image = fitter->GetUnderexplainedImage(); } } if (best_candidate.IsNull()) break; // fitter->SetPeakImage(peak_image); peak_image = best_candidate_peak_image; int i=0; std::vector< mitk::FiberBundle::Pointer > remaining_candidates; std::vector< std::string > remaining_candidate_files; for (auto fib : input_candidates) { if (fib!=best_candidate) { remaining_candidates.push_back(fib); remaining_candidate_files.push_back(candidate_tract_files.at(i)); } else name = ist::GetFilenameWithoutExtension(candidate_tract_files.at(i)); ++i; } input_candidates = remaining_candidates; candidate_tract_files = remaining_candidate_files; iteration++; std::streambuf *old = cout.rdbuf(); // <-- save std::stringstream ss; std::cout.rdbuf (ss.rdbuf()); // <-- redirect // Save winning candidate mitk::IOUtil::Save(best_candidate, out_folder + boost::lexical_cast(iteration) + "_" + name + ".fib"); peak_image_writer->SetInput(peak_image); peak_image_writer->SetFileName(out_folder + boost::lexical_cast(iteration) + "_" + name + ".nrrd"); peak_image_writer->Update(); // Calculate best overlap with reference masks for evaluation purposes float best_overlap = 0; int best_overlap_index = -1; i = 0; for (auto ref_mask : reference_masks) { float overlap = best_candidate->GetOverlap(ref_mask, false); if (overlap>best_overlap) { best_overlap = overlap; best_overlap_index = i; } ++i; } std::cout.rdbuf (old); // <-- restore logfile << "RMSE: " << setprecision(5) << rmse << " " << name << " " << num_peaks << "\n"; if (best_overlap_index>=0) logfile << "Best_overlap: " << setprecision(5) << best_overlap << " " << ist::GetFilenameWithoutExtension(anchor_mask_files.at(best_overlap_index)) << "\n"; else logfile << "No_overlap\n"; } } clock.Stop(); int h = clock.GetTotal()/3600; int m = ((int)clock.GetTotal()%3600)/60; int s = (int)clock.GetTotal()%60; MITK_INFO << "Plausibility estimation took " << h << "h, " << m << "m and " << s << "s"; logfile.close(); } catch (itk::ExceptionObject e) { std::cout << e; return EXIT_FAILURE; } catch (std::exception e) { std::cout << e.what(); return EXIT_FAILURE; } catch (...) { std::cout << "ERROR!?!"; return EXIT_FAILURE; } return EXIT_SUCCESS; } diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberFitView.cpp b/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberFitView.cpp index 3d67315158..8413aa80d9 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberFitView.cpp +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberFitView.cpp @@ -1,229 +1,265 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include "QmitkFiberFitView.h" #include #include #include #include #include #include #include #include #include #include +#include +#include const std::string QmitkFiberFitView::VIEW_ID = "org.mitk.views.fiberfit"; using namespace mitk; QmitkFiberFitView::QmitkFiberFitView() : QmitkAbstractView() , m_Controls( nullptr ) { } // Destructor QmitkFiberFitView::~QmitkFiberFitView() { } void QmitkFiberFitView::CreateQtPartControl( QWidget *parent ) { // build up qt view, unless already done if ( !m_Controls ) { // create GUI widgets from the Qt Designer's .ui file m_Controls = new Ui::QmitkFiberFitViewControls; m_Controls->setupUi( parent ); connect( m_Controls->m_StartButton, SIGNAL(clicked()), this, SLOT(StartFit()) ); connect( m_Controls->m_ImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(DataSelectionChanged()) ); connect( m_Controls->m_TractBox, SIGNAL(currentIndexChanged(int)), this, SLOT(DataSelectionChanged()) ); mitk::TNodePredicateDataType::Pointer isFib = mitk::TNodePredicateDataType::New(); - mitk::TNodePredicateDataType::Pointer isPeak = mitk::TNodePredicateDataType::New(); + mitk::TNodePredicateDataType::Pointer isImage = mitk::TNodePredicateDataType::New(); + mitk::NodePredicateDimension::Pointer is3D = mitk::NodePredicateDimension::New(3); m_Controls->m_TractBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_TractBox->SetPredicate(isFib); m_Controls->m_ImageBox->SetDataStorage(this->GetDataStorage()); - m_Controls->m_ImageBox->SetPredicate(isPeak); + m_Controls->m_ImageBox->SetPredicate( mitk::NodePredicateAnd::New(isImage, is3D) ); DataSelectionChanged(); } } void QmitkFiberFitView::DataSelectionChanged() { if (m_Controls->m_TractBox->GetSelectedNode().IsNull() || m_Controls->m_ImageBox->GetSelectedNode().IsNull()) m_Controls->m_StartButton->setEnabled(false); else m_Controls->m_StartButton->setEnabled(true); } void QmitkFiberFitView::SetFocus() { DataSelectionChanged(); } void QmitkFiberFitView::StartFit() { if (m_Controls->m_TractBox->GetSelectedNode().IsNull() || m_Controls->m_ImageBox->GetSelectedNode().IsNull()) return; mitk::FiberBundle::Pointer input_tracts = dynamic_cast(m_Controls->m_TractBox->GetSelectedNode()->GetData()); mitk::DataNode::Pointer node = m_Controls->m_ImageBox->GetSelectedNode(); itk::FitFibersToImageFilter::Pointer fitter = itk::FitFibersToImageFilter::New(); - mitk::Image::Pointer mitk_diff_image = dynamic_cast(node->GetData()); + mitk::Image::Pointer mitk_image = dynamic_cast(node->GetData()); mitk::PeakImage::Pointer mitk_peak_image = dynamic_cast(node->GetData()); if (mitk_peak_image.IsNotNull()) { typedef mitk::ImageToItk< mitk::PeakImage::ItkPeakImageType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(mitk_peak_image); caster->Update(); mitk::PeakImage::ItkPeakImageType::Pointer peak_image = caster->GetOutput(); fitter->SetPeakImage(peak_image); } else { - if (mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(mitk_diff_image)) + if (mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(mitk_image)) { - fitter->SetDiffImage(mitk::DiffusionPropertyHelper::GetItkVectorImage(mitk_diff_image)); + fitter->SetDiffImage(mitk::DiffusionPropertyHelper::GetItkVectorImage(mitk_image)); mitk::TensorModel<>* model = new mitk::TensorModel<>(); model->SetBvalue(1000); model->SetDiffusivity1(0.0010); model->SetDiffusivity2(0.00015); model->SetDiffusivity3(0.00015); - model->SetGradientList(mitk::DiffusionPropertyHelper::GetGradientContainer(mitk_diff_image)); + model->SetGradientList(mitk::DiffusionPropertyHelper::GetGradientContainer(mitk_image)); fitter->SetSignalModel(model); } else - return; + { + itk::FitFibersToImageFilter::DoubleImgType::Pointer scalar_image = itk::FitFibersToImageFilter::DoubleImgType::New(); + mitk::CastToItkImage(mitk_image, scalar_image); + fitter->SetScalarImage(scalar_image); + } } + if (m_Controls->m_ReguTypeBox->currentIndex()==0) + fitter->SetRegularization(VnlCostFunction::REGU::VOXEL_VARIANCE); + else if (m_Controls->m_ReguTypeBox->currentIndex()==1) + fitter->SetRegularization(VnlCostFunction::REGU::VARIANCE); + else if (m_Controls->m_ReguTypeBox->currentIndex()==2) + fitter->SetRegularization(VnlCostFunction::REGU::MSM); + else if (m_Controls->m_ReguTypeBox->currentIndex()==3) + fitter->SetRegularization(VnlCostFunction::REGU::LASSO); + else if (m_Controls->m_ReguTypeBox->currentIndex()==4) + fitter->SetRegularization(VnlCostFunction::REGU::NONE); + fitter->SetTractograms({input_tracts}); fitter->SetFitIndividualFibers(true); fitter->SetMaxIterations(20); fitter->SetVerbose(true); fitter->SetGradientTolerance(1e-5); fitter->SetLambda(m_Controls->m_ReguBox->value()); fitter->SetFilterOutliers(m_Controls->m_OutliersBox->isChecked()); fitter->Update(); mitk::FiberBundle::Pointer output_tracts = fitter->GetTractograms().at(0); mitk::DataNode::Pointer new_node = mitk::DataNode::New(); new_node->SetData(output_tracts); new_node->SetName("Fitted"); this->GetDataStorage()->Add(new_node, node); m_Controls->m_TractBox->GetSelectedNode()->SetVisibility(false); if (m_Controls->m_ResidualsBox->isChecked() && mitk_peak_image.IsNotNull()) { { mitk::PeakImage::ItkPeakImageType::Pointer itk_image = fitter->GetFittedImage(); mitk::Image::Pointer mitk_image = dynamic_cast(PeakImage::New().GetPointer()); mitk::CastToMitkImage(itk_image, mitk_image); mitk_image->SetVolume(itk_image->GetBufferPointer()); mitk::DataNode::Pointer new_node = mitk::DataNode::New(); new_node->SetData(mitk_image); new_node->SetName("Fitted"); this->GetDataStorage()->Add(new_node, node); } { mitk::PeakImage::ItkPeakImageType::Pointer itk_image = fitter->GetResidualImage(); mitk::Image::Pointer mitk_image = dynamic_cast(PeakImage::New().GetPointer()); mitk::CastToMitkImage(itk_image, mitk_image); mitk_image->SetVolume(itk_image->GetBufferPointer()); mitk::DataNode::Pointer new_node = mitk::DataNode::New(); new_node->SetData(mitk_image); new_node->SetName("Residual"); this->GetDataStorage()->Add(new_node, node); } { mitk::PeakImage::ItkPeakImageType::Pointer itk_image = fitter->GetUnderexplainedImage(); mitk::Image::Pointer mitk_image = dynamic_cast(PeakImage::New().GetPointer()); mitk::CastToMitkImage(itk_image, mitk_image); mitk_image->SetVolume(itk_image->GetBufferPointer()); mitk::DataNode::Pointer new_node = mitk::DataNode::New(); new_node->SetData(mitk_image); new_node->SetName("Underexplained"); this->GetDataStorage()->Add(new_node, node); } { mitk::PeakImage::ItkPeakImageType::Pointer itk_image = fitter->GetOverexplainedImage(); mitk::Image::Pointer mitk_image = dynamic_cast(PeakImage::New().GetPointer()); mitk::CastToMitkImage(itk_image, mitk_image); mitk_image->SetVolume(itk_image->GetBufferPointer()); mitk::DataNode::Pointer new_node = mitk::DataNode::New(); new_node->SetData(mitk_image); new_node->SetName("Overexplained"); this->GetDataStorage()->Add(new_node, node); } } - else if (m_Controls->m_ResidualsBox->isChecked() && mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(mitk_diff_image)) + else if (m_Controls->m_ResidualsBox->isChecked() && mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(mitk_image)) { { mitk::Image::Pointer outImage = mitk::GrabItkImageMemory( fitter->GetFittedImageDiff().GetPointer() ); - mitk::DiffusionPropertyHelper::CopyProperties(mitk_diff_image, outImage, true); + mitk::DiffusionPropertyHelper::CopyProperties(mitk_image, outImage, true); mitk::DiffusionPropertyHelper propertyHelper( outImage ); propertyHelper.InitializeImage(); mitk::DataNode::Pointer new_node = mitk::DataNode::New(); new_node->SetData(outImage); new_node->SetName("Fitted"); this->GetDataStorage()->Add(new_node, node); } { mitk::Image::Pointer outImage = mitk::GrabItkImageMemory( fitter->GetResidualImageDiff().GetPointer() ); - mitk::DiffusionPropertyHelper::CopyProperties(mitk_diff_image, outImage, true); + mitk::DiffusionPropertyHelper::CopyProperties(mitk_image, outImage, true); mitk::DiffusionPropertyHelper propertyHelper( outImage ); propertyHelper.InitializeImage(); mitk::DataNode::Pointer new_node = mitk::DataNode::New(); new_node->SetData(outImage); new_node->SetName("Residual"); this->GetDataStorage()->Add(new_node, node); } } + else if (m_Controls->m_ResidualsBox->isChecked()) + { + { + mitk::Image::Pointer outImage = mitk::GrabItkImageMemory( fitter->GetFittedImageScalar().GetPointer() ); + mitk::DataNode::Pointer new_node = mitk::DataNode::New(); + new_node->SetData(outImage); + new_node->SetName("Fitted"); + this->GetDataStorage()->Add(new_node, node); + } + + { + mitk::Image::Pointer outImage = mitk::GrabItkImageMemory( fitter->GetResidualImageScalar().GetPointer() ); + mitk::DataNode::Pointer new_node = mitk::DataNode::New(); + new_node->SetData(outImage); + new_node->SetName("Residual"); + this->GetDataStorage()->Add(new_node, node); + } + } } void QmitkFiberFitView::OnSelectionChanged(berry::IWorkbenchPart::Pointer /*part*/, const QList& ) { } diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberFitViewControls.ui b/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberFitViewControls.ui index 7c1c06263c..077bf184ed 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberFitViewControls.ui +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberFitViewControls.ui @@ -1,179 +1,215 @@ QmitkFiberFitViewControls 0 0 484 574 Form QFrame::NoFrame QFrame::Raised 0 0 0 0 6 - - - - Select a peak or raw diffusion-weighted image. - - - - + - true + false - - + + - Peak Image: + Output Residuals: - - - - Suppress Outliers: - - + + - + - Modifier for regularization. + Weight for regularization. 999999.000000000000000 0.100000000000000 - 1.000000000000000 + 0.100000000000000 - + + + + Tractogram: + + + + false 0 0 200 16777215 11 Start - - - - Tractogram: + + + + - - - - + λ: - - + + - Output Residuals: + Image: - + false + + + + Suppress Outliers: + + + + + + + Regularization: + + + + + + + + Voxel-wise Variance + + + + + Variance + + + + + Mean Squared Magnitude + + + + + Lasso + + + + + None + + + + Qt::Vertical 20 40 QmitkDataStorageComboBox QComboBox
QmitkDataStorageComboBox.h
 diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberQuantificationViewControls.ui b/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberQuantificationViewControls.ui index 8435a5d6bd..539ed2cc15 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberQuantificationViewControls.ui +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.fiberprocessing/src/internal/QmitkFiberQuantificationViewControls.ui @@ -1,395 +1,411 @@ QmitkFiberQuantificationViewControls 0 0 365 581 Form 25 - - + + - Input Data + Fiber-derived images - + 0 0 0 0 - - - - - - - - + + + + false + + + + 0 + 0 + + + + + 200 + 16777215 + + + + + 11 + + + + Perform selected operation on all selected fiber bundles. + - Tractogram: + Generate Image - - - - Reference Image: + + + + + 0 + 0 + + + + Upsampling factor + + + 1 + + + 0.100000000000000 + + + 10.000000000000000 + + + 0.100000000000000 + + + 1.000000000000000 + + + + + 0 + 0 + + + + + Tract Density Image (TDI) + + + + + Normalized TDI + + + + + Binary Envelope + + + + + Fiber Bundle Image + + + + + Fiber Endings Image + + + + + Fiber Endings Pointset + + + + Principal Fiber Directions 0 0 0 0 QFrame::NoFrame QFrame::Raised 0 0 0 0 0 0 Fiber directions with an angle smaller than the defined threshold are clustered. 2 0.000000000000000 90.000000000000000 1.000000000000000 30.000000000000000 0 0 <html><head/><body><p>Directions shorter than the defined threshold are discarded.</p></body></html> 3 1.000000000000000 0.100000000000000 0.300000000000000 Angular Threshold: Max. clusters: Size Threshold: 0 0 Maximum number of fiber directions per voxel. 100 3 Normalization: 0 0 + + 0 + Global maximum Single vector Voxel-wise maximum 0 0 Image containing the number of distinct fiber clusters per voxel. Output #Directions per Voxel - true + false false Generate Directions - - + + - Fiber-derived images + Input Data - + 0 0 0 0 - - - - false - - - - 0 - 0 - - - - - 200 - 16777215 - - - - - 11 - - - - Perform selected operation on all selected fiber bundles. - - - Generate Image - - - - - - - 0 - 0 - - - - Upsampling factor - - - 1 - - - 0.100000000000000 - - - 10.000000000000000 - - - 0.100000000000000 - - - 1.000000000000000 + + + + + + + + + Tractogram: - - - - - 0 - 0 - + + + + Reference Image: - - - Tract Density Image (TDI) - - - - - Normalized TDI - - - - - Binary Envelope - - - - - Fiber Bundle Image - - - - - Fiber Endings Image - - - - - Fiber Endings Pointset - - + + + + Qt::Vertical + + + + 20 + 40 + + + + QmitkDataStorageComboBox QComboBox
QmitkDataStorageComboBox.h
 QmitkDataStorageComboBoxWithSelectNone QComboBox
QmitkDataStorageComboBoxWithSelectNone.h
 diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp index ba2f299036..4877541e23 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp @@ -1,969 +1,971 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ // Blueberry #include #include #include // Qmitk #include "QmitkStreamlineTrackingView.h" #include "QmitkStdMultiWidget.h" // Qt #include // MITK #include #include #include #include #include #include #include #include #include #include #include #include #include // VTK #include #include #include #include #include #include #include #include #include const std::string QmitkStreamlineTrackingView::VIEW_ID = "org.mitk.views.streamlinetracking"; const std::string id_DataManager = "org.mitk.views.datamanager"; using namespace berry; QmitkStreamlineTrackingWorker::QmitkStreamlineTrackingWorker(QmitkStreamlineTrackingView* view) : m_View(view) { } void QmitkStreamlineTrackingWorker::run() { m_View->m_Tracker->Update(); m_View->m_TrackingThread.quit(); } QmitkStreamlineTrackingView::QmitkStreamlineTrackingView() : m_TrackingWorker(this) , m_Controls(nullptr) , m_FirstTensorProbRun(true) , m_FirstInteractiveRun(true) , m_TrackingHandler(nullptr) , m_ThreadIsRunning(false) , m_DeleteTrackingHandler(false) , m_Visible(false) + , m_LastPrior("") + , m_TrackingPriorHandler(nullptr) { m_TrackingWorker.moveToThread(&m_TrackingThread); connect(&m_TrackingThread, SIGNAL(started()), this, SLOT(BeforeThread())); connect(&m_TrackingThread, SIGNAL(started()), &m_TrackingWorker, SLOT(run())); connect(&m_TrackingThread, SIGNAL(finished()), this, SLOT(AfterThread())); m_TrackingTimer = new QTimer(this); } // Destructor QmitkStreamlineTrackingView::~QmitkStreamlineTrackingView() { if (m_Tracker.IsNull()) return; m_Tracker->SetStopTracking(true); m_TrackingThread.wait(); } void QmitkStreamlineTrackingView::CreateQtPartControl( QWidget *parent ) { if ( !m_Controls ) { // create GUI widgets from the Qt Designer's .ui file m_Controls = new Ui::QmitkStreamlineTrackingViewControls; m_Controls->setupUi( parent ); m_Controls->m_FaImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_SeedImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_MaskImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_TargetImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_PriorImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_StopImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_ForestBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_ExclusionImageBox->SetDataStorage(this->GetDataStorage()); mitk::TNodePredicateDataType::Pointer isPeakImagePredicate = mitk::TNodePredicateDataType::New(); mitk::TNodePredicateDataType::Pointer isImagePredicate = mitk::TNodePredicateDataType::New(); mitk::TNodePredicateDataType::Pointer isTractographyForest = mitk::TNodePredicateDataType::New(); mitk::NodePredicateProperty::Pointer isBinaryPredicate = mitk::NodePredicateProperty::New("binary", mitk::BoolProperty::New(true)); mitk::NodePredicateNot::Pointer isNotBinaryPredicate = mitk::NodePredicateNot::New( isBinaryPredicate ); mitk::NodePredicateAnd::Pointer isNotABinaryImagePredicate = mitk::NodePredicateAnd::New( isImagePredicate, isNotBinaryPredicate ); mitk::NodePredicateDimension::Pointer dimensionPredicate = mitk::NodePredicateDimension::New(3); m_Controls->m_ForestBox->SetPredicate(isTractographyForest); m_Controls->m_FaImageBox->SetPredicate( mitk::NodePredicateAnd::New(isNotABinaryImagePredicate, dimensionPredicate) ); m_Controls->m_FaImageBox->SetZeroEntryText("--"); m_Controls->m_SeedImageBox->SetPredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_SeedImageBox->SetZeroEntryText("--"); m_Controls->m_MaskImageBox->SetPredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_MaskImageBox->SetZeroEntryText("--"); m_Controls->m_StopImageBox->SetPredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_StopImageBox->SetZeroEntryText("--"); m_Controls->m_TargetImageBox->SetPredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_TargetImageBox->SetZeroEntryText("--"); m_Controls->m_PriorImageBox->SetPredicate( isPeakImagePredicate ); m_Controls->m_PriorImageBox->SetZeroEntryText("--"); m_Controls->m_ExclusionImageBox->SetPredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_ExclusionImageBox->SetZeroEntryText("--"); connect( m_TrackingTimer, SIGNAL(timeout()), this, SLOT(TimerUpdate()) ); connect( m_Controls->commandLinkButton_2, SIGNAL(clicked()), this, SLOT(StopTractography()) ); connect( m_Controls->commandLinkButton, SIGNAL(clicked()), this, SLOT(DoFiberTracking()) ); connect( m_Controls->m_InteractiveBox, SIGNAL(stateChanged(int)), this, SLOT(ToggleInteractive()) ); connect( m_Controls->m_ModeBox, SIGNAL(currentIndexChanged(int)), this, SLOT(UpdateGui()) ); connect( m_Controls->m_FaImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(DeleteTrackingHandler()) ); connect( m_Controls->m_ModeBox, SIGNAL(currentIndexChanged(int)), this, SLOT(DeleteTrackingHandler()) ); connect( m_Controls->m_OutputProbMap, SIGNAL(stateChanged(int)), this, SLOT(OutputStyleSwitched()) ); connect( m_Controls->m_SeedImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_ModeBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_StopImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_TargetImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_PriorImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_ExclusionImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_MaskImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FaImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_ForestBox, SIGNAL(currentIndexChanged(int)), this, SLOT(ForestSwitched()) ); connect( m_Controls->m_ForestBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SeedsPerVoxelBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_NumFibersBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_ScalarThresholdBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_OdfCutoffBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_StepSizeBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SamplingDistanceBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_AngularThresholdBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_MinTractLengthBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_fBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_gBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_NumSamplesBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SeedRadiusBox, SIGNAL(valueChanged(double)), this, SLOT(InteractiveSeedChanged()) ); connect( m_Controls->m_NumSeedsBox, SIGNAL(valueChanged(int)), this, SLOT(InteractiveSeedChanged()) ); connect( m_Controls->m_OutputProbMap, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SharpenOdfsBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_InterpolationBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_MaskInterpolationBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FlipXBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FlipYBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FlipZBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FrontalSamplesBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_StopVotesBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_LoopCheckBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_TrialsPerSeedBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_EpConstraintsBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); StartStopTrackingGui(false); } UpdateGui(); } void QmitkStreamlineTrackingView::StopTractography() { if (m_Tracker.IsNull()) return; m_Tracker->SetStopTracking(true); } void QmitkStreamlineTrackingView::TimerUpdate() { if (m_Tracker.IsNull()) return; QString status_text(m_Tracker->GetStatusText().c_str()); m_Controls->m_StatusTextBox->setText(status_text); } void QmitkStreamlineTrackingView::BeforeThread() { m_TrackingTimer->start(1000); } void QmitkStreamlineTrackingView::AfterThread() { m_TrackingTimer->stop(); if (!m_Tracker->GetUseOutputProbabilityMap()) { vtkSmartPointer fiberBundle = m_Tracker->GetFiberPolyData(); if (!m_Controls->m_InteractiveBox->isChecked() && fiberBundle->GetNumberOfLines() == 0) { QMessageBox warnBox; warnBox.setWindowTitle("Warning"); warnBox.setText("No fiberbundle was generated!"); warnBox.setDetailedText("No fibers were generated using the chosen parameters. Typical reasons are:\n\n- Cutoff too high. Some images feature very low FA/GFA/peak size. Try to lower this parameter.\n- Angular threshold too strict. Try to increase this parameter.\n- A small step sizes also means many steps to go wrong. Especially in the case of probabilistic tractography. Try to adjust the angular threshold."); warnBox.setIcon(QMessageBox::Warning); warnBox.exec(); if (m_InteractivePointSetNode.IsNotNull()) m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,1,1)); StartStopTrackingGui(false); if (m_DeleteTrackingHandler) DeleteTrackingHandler(); UpdateGui(); return; } mitk::FiberBundle::Pointer fib = mitk::FiberBundle::New(fiberBundle); fib->SetReferenceGeometry(dynamic_cast(m_ParentNode->GetData())->GetGeometry()); if (m_Controls->m_ResampleFibersBox->isChecked() && fiberBundle->GetNumberOfLines()>0) fib->Compress(m_Controls->m_FiberErrorBox->value()); fib->ColorFibersByOrientation(); m_Tracker->SetDicomProperties(fib); if (m_Controls->m_InteractiveBox->isChecked()) { if (m_InteractiveNode.IsNull()) { m_InteractiveNode = mitk::DataNode::New(); QString name("Interactive"); m_InteractiveNode->SetName(name.toStdString()); GetDataStorage()->Add(m_InteractiveNode); } m_InteractiveNode->SetData(fib); m_InteractiveNode->SetFloatProperty("Fiber2DSliceThickness", m_Tracker->GetMinVoxelSize()/2); if (auto renderWindowPart = this->GetRenderWindowPart()) renderWindowPart->RequestUpdate(); } else { mitk::DataNode::Pointer node = mitk::DataNode::New(); node->SetData(fib); QString name("FiberBundle_"); name += m_ParentNode->GetName().c_str(); name += "_Streamline"; node->SetName(name.toStdString()); node->SetFloatProperty("Fiber2DSliceThickness", m_Tracker->GetMinVoxelSize()/2); GetDataStorage()->Add(node, m_ParentNode); } } else { TrackerType::ItkDoubleImgType::Pointer outImg = m_Tracker->GetOutputProbabilityMap(); mitk::Image::Pointer img = mitk::Image::New(); img->InitializeByItk(outImg.GetPointer()); img->SetVolume(outImg->GetBufferPointer()); if (m_Controls->m_InteractiveBox->isChecked()) { if (m_InteractiveNode.IsNull()) { m_InteractiveNode = mitk::DataNode::New(); QString name("Interactive"); m_InteractiveNode->SetName(name.toStdString()); GetDataStorage()->Add(m_InteractiveNode); } m_InteractiveNode->SetData(img); mitk::LookupTable::Pointer lut = mitk::LookupTable::New(); lut->SetType(mitk::LookupTable::JET_TRANSPARENT); mitk::LookupTableProperty::Pointer lut_prop = mitk::LookupTableProperty::New(); lut_prop->SetLookupTable(lut); m_InteractiveNode->SetProperty("LookupTable", lut_prop); m_InteractiveNode->SetProperty("opacity", mitk::FloatProperty::New(0.5)); m_InteractiveNode->SetFloatProperty("Fiber2DSliceThickness", m_Tracker->GetMinVoxelSize()/2); if (auto renderWindowPart = this->GetRenderWindowPart()) renderWindowPart->RequestUpdate(); } else { mitk::DataNode::Pointer node = mitk::DataNode::New(); node->SetData(img); QString name("ProbabilityMap_"); name += m_ParentNode->GetName().c_str(); node->SetName(name.toStdString()); mitk::LookupTable::Pointer lut = mitk::LookupTable::New(); lut->SetType(mitk::LookupTable::JET_TRANSPARENT); mitk::LookupTableProperty::Pointer lut_prop = mitk::LookupTableProperty::New(); lut_prop->SetLookupTable(lut); node->SetProperty("LookupTable", lut_prop); node->SetProperty("opacity", mitk::FloatProperty::New(0.5)); GetDataStorage()->Add(node, m_ParentNode); } } if (m_InteractivePointSetNode.IsNotNull()) m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,1,1)); StartStopTrackingGui(false); if (m_DeleteTrackingHandler) DeleteTrackingHandler(); UpdateGui(); } void QmitkStreamlineTrackingView::InteractiveSeedChanged(bool posChanged) { if (m_ThreadIsRunning || !m_Visible) return; if (!posChanged && (!m_Controls->m_InteractiveBox->isChecked() || !m_Controls->m_ParamUpdateBox->isChecked())) return; std::srand(std::time(0)); m_SeedPoints.clear(); itk::Point world_pos = this->GetRenderWindowPart()->GetSelectedPosition(); m_SeedPoints.push_back(world_pos); float radius = m_Controls->m_SeedRadiusBox->value(); int num = m_Controls->m_NumSeedsBox->value(); mitk::PointSet::Pointer pointset = mitk::PointSet::New(); pointset->InsertPoint(0, world_pos); m_InteractivePointSetNode->SetProperty("pointsize", mitk::FloatProperty::New(radius*2)); m_InteractivePointSetNode->SetProperty("point 2D size", mitk::FloatProperty::New(radius*2)); m_InteractivePointSetNode->SetData(pointset); for (int i=1; i p; p[0] = rand()%1000-500; p[1] = rand()%1000-500; p[2] = rand()%1000-500; p.Normalize(); p *= radius; m_SeedPoints.push_back(world_pos+p); } m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,0,0)); DoFiberTracking(); } void QmitkStreamlineTrackingView::OnParameterChanged() { UpdateGui(); if (m_Controls->m_InteractiveBox->isChecked() && m_Controls->m_ParamUpdateBox->isChecked()) DoFiberTracking(); } void QmitkStreamlineTrackingView::ToggleInteractive() { UpdateGui(); m_Controls->m_SeedsPerVoxelBox->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); m_Controls->m_SeedsPerVoxelLabel->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); m_Controls->m_SeedImageBox->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); m_Controls->label_6->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); if ( m_Controls->m_InteractiveBox->isChecked() ) { if (m_FirstInteractiveRun) { QMessageBox::information(nullptr, "Information", "Place and move a spherical seed region anywhere in the image by left-clicking and dragging. If the seed region is colored red, tracking is in progress. If the seed region is colored white, tracking is finished.\nPlacing the seed region for the first time in a newly selected dataset might cause a short delay, since the tracker needs to be initialized."); m_FirstInteractiveRun = false; } QApplication::setOverrideCursor(Qt::PointingHandCursor); QApplication::processEvents(); m_InteractivePointSetNode = mitk::DataNode::New(); m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,1,1)); m_InteractivePointSetNode->SetName("InteractiveSeedRegion"); mitk::PointSetShapeProperty::Pointer shape_prop = mitk::PointSetShapeProperty::New(); shape_prop->SetValue(mitk::PointSetShapeProperty::PointSetShape::CIRCLE); m_InteractivePointSetNode->SetProperty("Pointset.2D.shape", shape_prop); GetDataStorage()->Add(m_InteractivePointSetNode); m_SliceChangeListener.RenderWindowPartActivated(this->GetRenderWindowPart()); connect(&m_SliceChangeListener, SIGNAL(SliceChanged()), this, SLOT(OnSliceChanged())); } else { QApplication::restoreOverrideCursor(); QApplication::processEvents(); m_InteractiveNode = nullptr; m_InteractivePointSetNode = nullptr; m_SliceChangeListener.RenderWindowPartActivated(this->GetRenderWindowPart()); disconnect(&m_SliceChangeListener, SIGNAL(SliceChanged()), this, SLOT(OnSliceChanged())); } } void QmitkStreamlineTrackingView::Activated() { } void QmitkStreamlineTrackingView::Deactivated() { } void QmitkStreamlineTrackingView::Visible() { m_Visible = true; } void QmitkStreamlineTrackingView::Hidden() { m_Visible = false; m_Controls->m_InteractiveBox->setChecked(false); ToggleInteractive(); } void QmitkStreamlineTrackingView::OnSliceChanged() { InteractiveSeedChanged(true); } void QmitkStreamlineTrackingView::SetFocus() { } void QmitkStreamlineTrackingView::DeleteTrackingHandler() { if (!m_ThreadIsRunning && m_TrackingHandler != nullptr) { delete m_TrackingHandler; m_TrackingHandler = nullptr; m_DeleteTrackingHandler = false; + m_LastPrior = ""; + if (m_TrackingPriorHandler != nullptr) + delete m_TrackingPriorHandler; } else if (m_ThreadIsRunning) { m_DeleteTrackingHandler = true; } } void QmitkStreamlineTrackingView::ForestSwitched() { DeleteTrackingHandler(); } void QmitkStreamlineTrackingView::OutputStyleSwitched() { if (m_InteractiveNode.IsNotNull()) GetDataStorage()->Remove(m_InteractiveNode); m_InteractiveNode = nullptr; } void QmitkStreamlineTrackingView::OnSelectionChanged( berry::IWorkbenchPart::Pointer , const QList& nodes ) { std::vector< mitk::DataNode::Pointer > last_nodes = m_InputImageNodes; m_InputImageNodes.clear(); m_InputImages.clear(); m_AdditionalInputImages.clear(); bool retrack = false; for( auto node : nodes ) { if( node.IsNotNull() && dynamic_cast(node->GetData()) ) { if( dynamic_cast(node->GetData()) ) { m_InputImageNodes.push_back(node); m_InputImages.push_back(dynamic_cast(node->GetData())); retrack = true; } else if ( dynamic_cast(node->GetData()) ) { m_InputImageNodes.push_back(node); m_InputImages.push_back(dynamic_cast(node->GetData())); retrack = true; } else if ( mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage( dynamic_cast(node->GetData())) ) { m_InputImageNodes.push_back(node); m_InputImages.push_back(dynamic_cast(node->GetData())); retrack = true; } else { mitk::Image* img = dynamic_cast(node->GetData()); if (img!=nullptr) { int dim = img->GetDimension(); unsigned int* dimensions = img->GetDimensions(); if (dim==4 && dimensions[3]%3==0) { m_InputImageNodes.push_back(node); m_InputImages.push_back(dynamic_cast(node->GetData())); retrack = true; } else if (dim==3) { m_AdditionalInputImages.push_back(dynamic_cast(node->GetData())); } } } } } // sometimes the OnSelectionChanged event is sent twice and actually no selection has changed for the first event. We need to catch that. if (last_nodes.size() == m_InputImageNodes.size()) { bool same_nodes = true; for (unsigned int i=0; im_TensorImageLabel->setText("select in data-manager"); m_Controls->m_fBox->setEnabled(false); m_Controls->m_fLabel->setEnabled(false); m_Controls->m_gBox->setEnabled(false); m_Controls->m_gLabel->setEnabled(false); m_Controls->m_FaImageBox->setEnabled(true); m_Controls->mFaImageLabel->setEnabled(true); m_Controls->m_OdfCutoffBox->setEnabled(false); m_Controls->m_OdfCutoffLabel->setEnabled(false); m_Controls->m_SharpenOdfsBox->setEnabled(false); m_Controls->m_ForestBox->setVisible(false); m_Controls->m_ForestLabel->setVisible(false); m_Controls->commandLinkButton->setEnabled(false); m_Controls->m_TrialsPerSeedBox->setEnabled(false); m_Controls->m_TrialsPerSeedLabel->setEnabled(false); m_Controls->m_TargetImageBox->setVisible(false); m_Controls->m_TargetImageLabel->setVisible(false); if (m_Controls->m_InteractiveBox->isChecked()) { m_Controls->m_InteractiveSeedingFrame->setVisible(true); m_Controls->m_StaticSeedingFrame->setVisible(false); m_Controls->commandLinkButton_2->setVisible(false); m_Controls->commandLinkButton->setVisible(false); } else { m_Controls->m_InteractiveSeedingFrame->setVisible(false); m_Controls->m_StaticSeedingFrame->setVisible(true); m_Controls->commandLinkButton_2->setVisible(m_ThreadIsRunning); m_Controls->commandLinkButton->setVisible(!m_ThreadIsRunning); } if (m_Controls->m_EpConstraintsBox->currentIndex()>0) { m_Controls->m_TargetImageBox->setVisible(true); m_Controls->m_TargetImageLabel->setVisible(true); } // trials per seed are only important for probabilistic tractography if (m_Controls->m_ModeBox->currentIndex()==1) { m_Controls->m_TrialsPerSeedBox->setEnabled(true); m_Controls->m_TrialsPerSeedLabel->setEnabled(true); } if(!m_InputImageNodes.empty()) { if (m_InputImageNodes.size()>1) m_Controls->m_TensorImageLabel->setText( ( std::to_string(m_InputImageNodes.size()) + " images selected").c_str() ); else m_Controls->m_TensorImageLabel->setText(m_InputImageNodes.at(0)->GetName().c_str()); m_Controls->commandLinkButton->setEnabled(!m_Controls->m_InteractiveBox->isChecked() && !m_ThreadIsRunning); m_Controls->m_ScalarThresholdBox->setEnabled(true); m_Controls->m_FaThresholdLabel->setEnabled(true); if ( dynamic_cast(m_InputImageNodes.at(0)->GetData()) ) { m_Controls->m_fBox->setEnabled(true); m_Controls->m_fLabel->setEnabled(true); m_Controls->m_gBox->setEnabled(true); m_Controls->m_gLabel->setEnabled(true); } else if ( dynamic_cast(m_InputImageNodes.at(0)->GetData()) ) { m_Controls->m_OdfCutoffBox->setEnabled(true); m_Controls->m_OdfCutoffLabel->setEnabled(true); m_Controls->m_SharpenOdfsBox->setEnabled(true); } else if ( mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage( dynamic_cast(m_InputImageNodes.at(0)->GetData())) ) { m_Controls->m_ForestBox->setVisible(true); m_Controls->m_ForestLabel->setVisible(true); m_Controls->m_ScalarThresholdBox->setEnabled(false); m_Controls->m_FaThresholdLabel->setEnabled(false); } } } void QmitkStreamlineTrackingView::StartStopTrackingGui(bool start) { m_ThreadIsRunning = start; if (!m_Controls->m_InteractiveBox->isChecked()) { m_Controls->commandLinkButton_2->setVisible(start); m_Controls->commandLinkButton->setVisible(!start); m_Controls->m_InteractiveBox->setEnabled(!start); m_Controls->m_StatusTextBox->setVisible(start); } } void QmitkStreamlineTrackingView::DoFiberTracking() { if (m_ThreadIsRunning || m_InputImages.empty() || !m_Visible) return; if (m_Controls->m_InteractiveBox->isChecked() && m_SeedPoints.empty()) return; StartStopTrackingGui(true); m_Tracker = TrackerType::New(); if( dynamic_cast(m_InputImageNodes.at(0)->GetData()) ) { if (m_Controls->m_ModeBox->currentIndex()==1) { if (m_InputImages.size()>1) { QMessageBox::information(nullptr, "Information", "Probabilistic tensor tractography is only implemented for single-tensor mode!"); StartStopTrackingGui(false); return; } -// if (m_FirstTensorProbRun) -// { -// QMessageBox::information(nullptr, "Information", "Internally calculating ODF from tensor image and performing probabilistic ODF tractography. ODFs are sharpened (min-max normalized and raised to the power of 4). TEND parameters are ignored."); -// m_FirstTensorProbRun = false; -// } - if (m_TrackingHandler==nullptr) { m_TrackingHandler = new mitk::TrackingHandlerOdf(); mitk::TensorImage::ItkTensorImageType::Pointer itkImg = mitk::TensorImage::ItkTensorImageType::New(); mitk::CastToItkImage(m_InputImages.at(0), itkImg); typedef itk::TensorImageToOdfImageFilter< float, float > FilterType; FilterType::Pointer filter = FilterType::New(); filter->SetInput( itkImg ); filter->Update(); dynamic_cast(m_TrackingHandler)->SetOdfImage(filter->GetOutput()); if (m_Controls->m_FaImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer itkImg = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_FaImageBox->GetSelectedNode()->GetData()), itkImg); dynamic_cast(m_TrackingHandler)->SetGfaImage(itkImg); } } dynamic_cast(m_TrackingHandler)->SetGfaThreshold(m_Controls->m_ScalarThresholdBox->value()); dynamic_cast(m_TrackingHandler)->SetOdfThreshold(0); dynamic_cast(m_TrackingHandler)->SetSharpenOdfs(true); dynamic_cast(m_TrackingHandler)->SetIsOdfFromTensor(true); } else { if (m_TrackingHandler==nullptr) { m_TrackingHandler = new mitk::TrackingHandlerTensor(); for (int i=0; i<(int)m_InputImages.size(); i++) { typedef mitk::ImageToItk< mitk::TrackingHandlerTensor::ItkTensorImageType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(m_InputImages.at(i)); caster->Update(); mitk::TrackingHandlerTensor::ItkTensorImageType::ConstPointer itkImg = caster->GetOutput(); dynamic_cast(m_TrackingHandler)->AddTensorImage(itkImg); } if (m_Controls->m_FaImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer itkImg = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_FaImageBox->GetSelectedNode()->GetData()), itkImg); dynamic_cast(m_TrackingHandler)->SetFaImage(itkImg); } } dynamic_cast(m_TrackingHandler)->SetFaThreshold(m_Controls->m_ScalarThresholdBox->value()); dynamic_cast(m_TrackingHandler)->SetF((float)m_Controls->m_fBox->value()); dynamic_cast(m_TrackingHandler)->SetG((float)m_Controls->m_gBox->value()); } } else if ( dynamic_cast(m_InputImageNodes.at(0)->GetData()) ) { if (m_TrackingHandler==nullptr) { m_TrackingHandler = new mitk::TrackingHandlerOdf(); mitk::TrackingHandlerOdf::ItkOdfImageType::Pointer itkImg = mitk::TrackingHandlerOdf::ItkOdfImageType::New(); mitk::CastToItkImage(m_InputImages.at(0), itkImg); dynamic_cast(m_TrackingHandler)->SetOdfImage(itkImg); if (m_Controls->m_FaImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer itkImg = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_FaImageBox->GetSelectedNode()->GetData()), itkImg); dynamic_cast(m_TrackingHandler)->SetGfaImage(itkImg); } } dynamic_cast(m_TrackingHandler)->SetGfaThreshold(m_Controls->m_ScalarThresholdBox->value()); dynamic_cast(m_TrackingHandler)->SetOdfThreshold(m_Controls->m_OdfCutoffBox->value()); dynamic_cast(m_TrackingHandler)->SetSharpenOdfs(m_Controls->m_SharpenOdfsBox->isChecked()); } else if ( mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage( dynamic_cast(m_InputImageNodes.at(0)->GetData())) ) { if ( m_Controls->m_ForestBox->GetSelectedNode().IsNull() ) { QMessageBox::information(nullptr, "Information", "Not random forest for machine learning based tractography (raw dMRI tractography) selected. Did you accidentally select the raw diffusion-weighted image in the datamanager?"); StartStopTrackingGui(false); return; } if (m_TrackingHandler==nullptr) { mitk::TractographyForest::Pointer forest = dynamic_cast(m_Controls->m_ForestBox->GetSelectedNode()->GetData()); mitk::Image::Pointer dwi = dynamic_cast(m_InputImageNodes.at(0)->GetData()); std::vector< std::vector< ItkFloatImageType::Pointer > > additionalFeatureImages; additionalFeatureImages.push_back(std::vector< ItkFloatImageType::Pointer >()); for (auto img : m_AdditionalInputImages) { ItkFloatImageType::Pointer itkimg = ItkFloatImageType::New(); mitk::CastToItkImage(img, itkimg); additionalFeatureImages.at(0).push_back(itkimg); } bool forest_valid = false; if (forest->GetNumFeatures()>=100) { int num_previous_directions = (forest->GetNumFeatures() - (100 + additionalFeatureImages.at(0).size()))/3; m_TrackingHandler = new mitk::TrackingHandlerRandomForest<6, 100>(); dynamic_cast*>(m_TrackingHandler)->AddDwi(dwi); dynamic_cast*>(m_TrackingHandler)->SetAdditionalFeatureImages(additionalFeatureImages); dynamic_cast*>(m_TrackingHandler)->SetForest(forest); dynamic_cast*>(m_TrackingHandler)->SetNumPreviousDirections(num_previous_directions); forest_valid = dynamic_cast*>(m_TrackingHandler)->IsForestValid(); } else { int num_previous_directions = (forest->GetNumFeatures() - (28 + additionalFeatureImages.at(0).size()))/3; m_TrackingHandler = new mitk::TrackingHandlerRandomForest<6, 28>(); dynamic_cast*>(m_TrackingHandler)->AddDwi(dwi); dynamic_cast*>(m_TrackingHandler)->SetAdditionalFeatureImages(additionalFeatureImages); dynamic_cast*>(m_TrackingHandler)->SetForest(forest); dynamic_cast*>(m_TrackingHandler)->SetNumPreviousDirections(num_previous_directions); forest_valid = dynamic_cast*>(m_TrackingHandler)->IsForestValid(); } if (!forest_valid) { QMessageBox::information(nullptr, "Information", "Random forest is invalid. The forest signatue does not match the parameters of TrackingHandlerRandomForest."); StartStopTrackingGui(false); return; } } } else { if (m_Controls->m_ModeBox->currentIndex()==1) { QMessageBox::information(nullptr, "Information", "Probabilstic tractography is not implemented for peak images."); StartStopTrackingGui(false); return; } try { if (m_TrackingHandler==nullptr) { typedef mitk::ImageToItk< mitk::TrackingHandlerPeaks::PeakImgType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(m_InputImages.at(0)); caster->SetCopyMemFlag(true); caster->Update(); mitk::TrackingHandlerPeaks::PeakImgType::Pointer itkImg = caster->GetOutput(); m_TrackingHandler = new mitk::TrackingHandlerPeaks(); dynamic_cast(m_TrackingHandler)->SetPeakImage(itkImg); } dynamic_cast(m_TrackingHandler)->SetPeakThreshold(m_Controls->m_ScalarThresholdBox->value()); } catch(...) { QMessageBox::information(nullptr, "Error", "Peak tracker could not be initialized. Is your input image in the correct format (4D float image, peaks in the 4th dimension)?"); StartStopTrackingGui(false); return; } } m_TrackingHandler->SetFlipX(m_Controls->m_FlipXBox->isChecked()); m_TrackingHandler->SetFlipY(m_Controls->m_FlipYBox->isChecked()); m_TrackingHandler->SetFlipZ(m_Controls->m_FlipZBox->isChecked()); m_TrackingHandler->SetInterpolate(m_Controls->m_InterpolationBox->isChecked()); switch (m_Controls->m_ModeBox->currentIndex()) { case 0: m_TrackingHandler->SetMode(mitk::TrackingDataHandler::MODE::DETERMINISTIC); break; case 1: m_TrackingHandler->SetMode(mitk::TrackingDataHandler::MODE::PROBABILISTIC); break; default: m_TrackingHandler->SetMode(mitk::TrackingDataHandler::MODE::DETERMINISTIC); } if (m_Controls->m_InteractiveBox->isChecked()) { m_Tracker->SetSeedPoints(m_SeedPoints); } else if (m_Controls->m_SeedImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_SeedImageBox->GetSelectedNode()->GetData()), mask); m_Tracker->SetSeedImage(mask); } if (m_Controls->m_MaskImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_MaskImageBox->GetSelectedNode()->GetData()), mask); m_Tracker->SetMaskImage(mask); } if (m_Controls->m_StopImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_StopImageBox->GetSelectedNode()->GetData()), mask); m_Tracker->SetStoppingRegions(mask); } if (m_Controls->m_TargetImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_TargetImageBox->GetSelectedNode()->GetData()), mask); m_Tracker->SetTargetRegions(mask); } if (m_Controls->m_PriorImageBox->GetSelectedNode().IsNotNull()) { - typedef mitk::ImageToItk< mitk::TrackingHandlerPeaks::PeakImgType > CasterType; - CasterType::Pointer caster = CasterType::New(); - caster->SetInput(dynamic_cast(m_Controls->m_PriorImageBox->GetSelectedNode()->GetData())); - caster->SetCopyMemFlag(true); - caster->Update(); - mitk::TrackingHandlerPeaks::PeakImgType::Pointer itkImg = caster->GetOutput(); - mitk::TrackingDataHandler* trackingPriorHandler = new mitk::TrackingHandlerPeaks(); - dynamic_cast(trackingPriorHandler)->SetPeakImage(itkImg); - dynamic_cast(trackingPriorHandler)->SetPeakThreshold(m_Controls->m_ScalarThresholdBox->value()); - - trackingPriorHandler->SetFlipX(m_Controls->m_FlipXBox->isChecked()); - trackingPriorHandler->SetFlipY(m_Controls->m_FlipYBox->isChecked()); - trackingPriorHandler->SetFlipZ(m_Controls->m_FlipZBox->isChecked()); - trackingPriorHandler->SetInterpolate(m_Controls->m_InterpolationBox->isChecked()); - trackingPriorHandler->SetMode(mitk::TrackingDataHandler::MODE::DETERMINISTIC); - - m_Tracker->SetTrackingPriorHandler(trackingPriorHandler); + if (m_LastPrior!=m_Controls->m_PriorImageBox->GetSelectedNode()->GetUID() || m_TrackingPriorHandler==nullptr) + { + typedef mitk::ImageToItk< mitk::TrackingHandlerPeaks::PeakImgType > CasterType; + CasterType::Pointer caster = CasterType::New(); + caster->SetInput(dynamic_cast(m_Controls->m_PriorImageBox->GetSelectedNode()->GetData())); + caster->SetCopyMemFlag(true); + caster->Update(); + mitk::TrackingHandlerPeaks::PeakImgType::Pointer itkImg = caster->GetOutput(); + m_TrackingPriorHandler = new mitk::TrackingHandlerPeaks(); + dynamic_cast(m_TrackingPriorHandler)->SetPeakImage(itkImg); + dynamic_cast(m_TrackingPriorHandler)->SetPeakThreshold(0.0); + m_LastPrior = m_Controls->m_PriorImageBox->GetSelectedNode()->GetUID(); + } + + m_TrackingPriorHandler->SetInterpolate(m_Controls->m_InterpolationBox->isChecked()); + m_TrackingPriorHandler->SetMode(mitk::TrackingDataHandler::MODE::DETERMINISTIC); + + m_Tracker->SetTrackingPriorHandler(m_TrackingPriorHandler); m_Tracker->SetTrackingPriorWeight(m_Controls->m_PriorWeightBox->value()); m_Tracker->SetTrackingPriorAsMask(m_Controls->m_PriorAsMaskBox->isChecked()); m_Tracker->SetIntroduceDirectionsFromPrior(m_Controls->m_NewDirectionsFromPriorBox->isChecked()); } + else if (m_Controls->m_PriorImageBox->GetSelectedNode().IsNull()) + m_Tracker->SetTrackingPriorHandler(nullptr); if (m_Controls->m_ExclusionImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_ExclusionImageBox->GetSelectedNode()->GetData()), mask); m_Tracker->SetExclusionRegions(mask); } // Endpoint constraints switch (m_Controls->m_EpConstraintsBox->currentIndex()) { case 0: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::NONE); m_Tracker->SetTargetRegions(nullptr); break; case 1: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET); break; case 2: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET_LABELDIFF); break; case 3: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_SEED_AND_TARGET); break; case 4: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::MIN_ONE_EP_IN_TARGET); break; case 5: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::ONE_EP_IN_TARGET); break; case 6: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::NO_EP_IN_TARGET); break; } if (m_Tracker->GetEndpointConstraint()!=itk::StreamlineTrackingFilter::EndpointConstraints::NONE && m_Controls->m_TargetImageBox->GetSelectedNode().IsNull()) { QMessageBox::information(nullptr, "Error", "Endpoint constraints are used but no target image is set!"); StartStopTrackingGui(false); return; } else if (m_Tracker->GetEndpointConstraint()==itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_SEED_AND_TARGET && (m_Controls->m_SeedImageBox->GetSelectedNode().IsNull()|| m_Controls->m_TargetImageBox->GetSelectedNode().IsNull()) ) { QMessageBox::information(nullptr, "Error", "Endpoint constraint EPS_IN_SEED_AND_TARGET is used but no target or no seed image is set!"); StartStopTrackingGui(false); return; } m_Tracker->SetInterpolateMasks(m_Controls->m_MaskInterpolationBox->isChecked()); m_Tracker->SetVerbose(!m_Controls->m_InteractiveBox->isChecked()); m_Tracker->SetSeedsPerVoxel(m_Controls->m_SeedsPerVoxelBox->value()); m_Tracker->SetStepSize(m_Controls->m_StepSizeBox->value()); m_Tracker->SetSamplingDistance(m_Controls->m_SamplingDistanceBox->value()); m_Tracker->SetUseStopVotes(m_Controls->m_StopVotesBox->isChecked()); m_Tracker->SetOnlyForwardSamples(m_Controls->m_FrontalSamplesBox->isChecked()); m_Tracker->SetTrialsPerSeed(m_Controls->m_TrialsPerSeedBox->value()); m_Tracker->SetMaxNumTracts(m_Controls->m_NumFibersBox->value()); m_Tracker->SetNumberOfSamples(m_Controls->m_NumSamplesBox->value()); m_Tracker->SetTrackingHandler(m_TrackingHandler); m_Tracker->SetLoopCheck(m_Controls->m_LoopCheckBox->value()); m_Tracker->SetAngularThreshold(m_Controls->m_AngularThresholdBox->value()); m_Tracker->SetMinTractLength(m_Controls->m_MinTractLengthBox->value()); m_Tracker->SetUseOutputProbabilityMap(m_Controls->m_OutputProbMap->isChecked()); m_ParentNode = m_InputImageNodes.at(0); m_TrackingThread.start(QThread::LowestPriority); } diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.h b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.h index 0f6db98e80..d5d0d6ee99 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.h +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.h @@ -1,151 +1,153 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #ifndef QmitkStreamlineTrackingView_h #define QmitkStreamlineTrackingView_h #include #include "ui_QmitkStreamlineTrackingViewControls.h" #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include class QmitkStreamlineTrackingView; class QmitkStreamlineTrackingWorker : public QObject { Q_OBJECT public: QmitkStreamlineTrackingWorker(QmitkStreamlineTrackingView* view); public slots: void run(); private: QmitkStreamlineTrackingView* m_View; }; /*! \brief View for tensor based deterministic streamline fiber tracking. */ class QmitkStreamlineTrackingView : public QmitkAbstractView, public mitk::ILifecycleAwarePart { // this is needed for all Qt objects that should have a Qt meta-object // (everything that derives from QObject and wants to have signal/slots) Q_OBJECT public: static const std::string VIEW_ID; typedef itk::Image< unsigned int, 3 > ItkUintImgType; typedef itk::Image< unsigned char, 3 > ItkUCharImageType; typedef itk::Image< float, 3 > ItkFloatImageType; typedef itk::StreamlineTrackingFilter TrackerType; QmitkStreamlineTrackingView(); virtual ~QmitkStreamlineTrackingView(); virtual void CreateQtPartControl(QWidget *parent) override; /// /// Sets the focus to an internal widget. /// virtual void SetFocus() override; TrackerType::Pointer m_Tracker; QmitkStreamlineTrackingWorker m_TrackingWorker; QThread m_TrackingThread; virtual void Activated() override; virtual void Deactivated() override; virtual void Visible() override; virtual void Hidden() override; protected slots: void DoFiberTracking(); ///< start fiber tracking void UpdateGui(); void ToggleInteractive(); void DeleteTrackingHandler(); void OnParameterChanged(); void InteractiveSeedChanged(bool posChanged=false); void ForestSwitched(); void OutputStyleSwitched(); void AfterThread(); ///< update gui etc. after tracking has finished void BeforeThread(); ///< start timer etc. void TimerUpdate(); void StopTractography(); void OnSliceChanged(); protected: /// \brief called by QmitkAbstractView when DataManager's selection has changed virtual void OnSelectionChanged(berry::IWorkbenchPart::Pointer part, const QList& nodes) override; Ui::QmitkStreamlineTrackingViewControls* m_Controls; protected slots: private: void StartStopTrackingGui(bool start); std::vector< itk::Point > m_SeedPoints; mitk::DataNode::Pointer m_ParentNode; mitk::DataNode::Pointer m_InteractiveNode; mitk::DataNode::Pointer m_InteractivePointSetNode; std::vector< mitk::DataNode::Pointer > m_InputImageNodes; ///< input image nodes std::vector< mitk::Image::ConstPointer > m_InputImages; ///< input images std::vector< mitk::Image::ConstPointer > m_AdditionalInputImages; bool m_FirstTensorProbRun; bool m_FirstInteractiveRun; mitk::TrackingDataHandler* m_TrackingHandler; bool m_ThreadIsRunning; QTimer* m_TrackingTimer; bool m_DeleteTrackingHandler; QmitkSliceNavigationListener m_SliceChangeListener; bool m_Visible; + mitk::Identifiable::UIDType m_LastPrior; + mitk::TrackingDataHandler* m_TrackingPriorHandler; }; #endif // _QMITKFIBERTRACKINGVIEW_H_INCLUDED