diff --git a/Modules/Bundles/org.mitk.gui.qt.diffusionimaging/src/internal/QmitkQBallReconstructionView.cpp b/Modules/Bundles/org.mitk.gui.qt.diffusionimaging/src/internal/QmitkQBallReconstructionView.cpp index 9f54ab5a54..0ef1d8d4ff 100644 --- a/Modules/Bundles/org.mitk.gui.qt.diffusionimaging/src/internal/QmitkQBallReconstructionView.cpp +++ b/Modules/Bundles/org.mitk.gui.qt.diffusionimaging/src/internal/QmitkQBallReconstructionView.cpp @@ -1,768 +1,778 @@ /*========================================================================= Program: Medical Imaging & Interaction Toolkit Module: $RCSfile$ Language: C++ Date: $Date: 2009-05-28 17:19:30 +0200 (Do, 28 Mai 2009) $ Version: $Revision: 17495 $ Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. See MITKCopyright.txt or http://www.mitk.org/copyright.html for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ //#define MBILOG_ENABLE_DEBUG #include "QmitkQBallReconstructionView.h" #include "mitkDiffusionImagingConfigure.h" // qt includes #include // itk includes #include "itkTimeProbe.h" // mitk includes #include "mitkProgressBar.h" #include "mitkStatusBar.h" #include "mitkNodePredicateDataType.h" #include "QmitkDataStorageComboBox.h" #include "QmitkStdMultiWidget.h" #include "itkDiffusionQballReconstructionImageFilter.h" #include "itkAnalyticalDiffusionQballReconstructionImageFilter.h" #include "itkVectorContainer.h" #include "mitkQBallImage.h" #include "mitkProperties.h" #include "mitkVtkResliceInterpolationProperty.h" #include "mitkLookupTable.h" #include "mitkLookupTableProperty.h" #include "mitkTransferFunction.h" #include "mitkTransferFunctionProperty.h" #include "mitkDataNodeObject.h" #include "mitkOdfNormalizationMethodProperty.h" #include "mitkOdfScaleByProperty.h" #include "berryIStructuredSelection.h" #include "berryIWorkbenchWindow.h" #include "berryISelectionService.h" #include const std::string QmitkQBallReconstructionView::VIEW_ID = "org.mitk.views.qballreconstruction"; #define DI_INFO MITK_INFO("DiffusionImaging") typedef float TTensorPixelType; const int QmitkQBallReconstructionView::nrconvkernels = 252; using namespace berry; struct QbrSelListener : ISelectionListener { berryObjectMacro(QbrSelListener); QbrSelListener(QmitkQBallReconstructionView* view) { m_View = view; } void DoSelectionChanged(ISelection::ConstPointer selection) { // save current selection in member variable m_View->m_CurrentSelection = selection.Cast(); // do something with the selected items if(m_View->m_CurrentSelection) { bool foundDwiVolume = false; // iterate selection for (IStructuredSelection::iterator i = m_View->m_CurrentSelection->Begin(); i != m_View->m_CurrentSelection->End(); ++i) { // extract datatree node if (mitk::DataNodeObject::Pointer nodeObj = i->Cast()) { mitk::DataNode::Pointer node = nodeObj->GetDataNode(); // only look at interesting types if(QString("DiffusionImage").compare(node->GetData()->GetNameOfClass())==0) { foundDwiVolume = true; } } } m_View->m_Controls->m_ButtonStandard->setEnabled(foundDwiVolume); } } void SelectionChanged(IWorkbenchPart::Pointer part, ISelection::ConstPointer selection) { // check, if selection comes from datamanager if (part) { QString partname(part->GetPartName().c_str()); if(partname.compare("Datamanager")==0) { // apply selection DoSelectionChanged(selection); } } } QmitkQBallReconstructionView* m_View; }; QmitkQBallReconstructionView::QmitkQBallReconstructionView() : QmitkFunctionality(), m_Controls(NULL), m_MultiWidget(NULL) { } //void QmitkQBallReconstructionView::OpactiyChanged(int value) //{ // if (m_CurrentSelection) // { // if (mitk::DataNodeObject::Pointer nodeObj = m_CurrentSelection->Begin()->Cast()) // { // mitk::DataNode::Pointer node = nodeObj->GetDataNode(); // if(QString("DiffusionImage").compare(node->GetData()->GetNameOfClass())==0) // { // node->SetIntProperty("DisplayChannel", value); // mitk::RenderingManager::GetInstance()->RequestUpdateAll(); // } // } // } //} // //void QmitkQBallReconstructionView::OpactiyActionChanged() //{ // if (m_CurrentSelection) // { // if (mitk::DataNodeObject::Pointer nodeObj = m_CurrentSelection->Begin()->Cast()) // { // mitk::DataNode::Pointer node = nodeObj->GetDataNode(); // if(QString("DiffusionImage").compare(node->GetData()->GetNameOfClass())==0) // { // int displayChannel = 0.0; // if(node->GetIntProperty("DisplayChannel", displayChannel)) // { // m_OpacitySlider->setValue(displayChannel); // } // } // } // } // // MITK_INFO << "changed"; //} QmitkQBallReconstructionView::~QmitkQBallReconstructionView() { this->GetSite()->GetWorkbenchWindow()->GetSelectionService()->RemovePostSelectionListener(/*"org.mitk.views.datamanager",*/ m_SelListener); } void QmitkQBallReconstructionView::CreateQtPartControl(QWidget *parent) { if (!m_Controls) { // create GUI widgets m_Controls = new Ui::QmitkQBallReconstructionViewControls; m_Controls->setupUi(parent); this->CreateConnections(); QStringList items; items << "2" << "4" << "6" << "8"; m_Controls->m_QBallReconstructionMaxLLevelComboBox->addItems(items); m_Controls->m_QBallReconstructionMaxLLevelComboBox->setCurrentIndex(1); MethodChoosen(m_Controls->m_QBallReconstructionMethodComboBox->currentIndex()); m_Controls->m_QBallReconstructionNumberThreadsSpinbox->setValue(8); #ifndef DIFFUSION_IMAGING_EXTENDED m_Controls->m_QBallReconstructionMethodComboBox->removeItem(3); #endif AdvancedCheckboxClicked(); // define data type for combobox //m_Controls->m_ImageSelector->SetDataStorage( this->GetDefaultDataStorage() ); //m_Controls->m_ImageSelector->SetPredicate( mitk::NodePredicateDataType::New("DiffusionImage") ); } m_SelListener = berry::ISelectionListener::Pointer(new QbrSelListener(this)); this->GetSite()->GetWorkbenchWindow()->GetSelectionService()->AddPostSelectionListener(/*"org.mitk.views.datamanager",*/ m_SelListener); berry::ISelection::ConstPointer sel( this->GetSite()->GetWorkbenchWindow()->GetSelectionService()->GetSelection("org.mitk.views.datamanager")); m_CurrentSelection = sel.Cast(); m_SelListener.Cast()->DoSelectionChanged(sel); } void QmitkQBallReconstructionView::StdMultiWidgetAvailable (QmitkStdMultiWidget &stdMultiWidget) { m_MultiWidget = &stdMultiWidget; } void QmitkQBallReconstructionView::StdMultiWidgetNotAvailable() { m_MultiWidget = NULL; } void QmitkQBallReconstructionView::CreateConnections() { if ( m_Controls ) { connect( (QObject*)(m_Controls->m_ButtonStandard), SIGNAL(clicked()), this, SLOT(ReconstructStandard()) ); connect( (QObject*)(m_Controls->m_AdvancedCheckbox), SIGNAL(clicked()), this, SLOT(AdvancedCheckboxClicked()) ); connect( (QObject*)(m_Controls->m_QBallReconstructionMethodComboBox), SIGNAL(currentIndexChanged(int)), this, SLOT(MethodChoosen(int)) ); } } void QmitkQBallReconstructionView::Activated() { QmitkFunctionality::Activated(); berry::ISelection::ConstPointer sel( this->GetSite()->GetWorkbenchWindow()->GetSelectionService()->GetSelection("org.mitk.views.datamanager")); m_CurrentSelection = sel.Cast(); m_SelListener.Cast()->DoSelectionChanged(sel); } void QmitkQBallReconstructionView::Deactivated() { QmitkFunctionality::Deactivated(); } void QmitkQBallReconstructionView::ReconstructStandard() { int index = m_Controls->m_QBallReconstructionMethodComboBox->currentIndex(); #ifndef DIFFUSION_IMAGING_EXTENDED if(index>=3) { index = index + 1; } #endif switch(index) { case 0: { // Numerical Reconstruct(0,0); break; } case 1: { // Standard Reconstruct(1,0); break; } case 2: { // Solid Angle Reconstruct(1,6); break; } case 3: { // Constrained Solid Angle Reconstruct(1,7); break; } case 4: { // ADC Reconstruct(1,4); break; } case 5: { // Raw Signal Reconstruct(1,5); break; } } } void QmitkQBallReconstructionView::MethodChoosen(int method) { switch(method) { case 0: m_Controls->m_Description->setText("Numerical recon. (Tuch2004)"); break; case 1: m_Controls->m_Description->setText("Spherical harmonics recon. (Desoteaux2007)"); break; case 2: m_Controls->m_Description->setText("SH recon. with solid angle consideration (Aganj2009)"); break; case 3: m_Controls->m_Description->setText("SH solid angle with non-neg. constraint (Goh2009)"); break; case 4: m_Controls->m_Description->setText("SH recon. of the plain ADC-profiles"); break; case 5: m_Controls->m_Description->setText("SH recon. of the raw diffusion signal"); break; } } void QmitkQBallReconstructionView::AdvancedCheckboxClicked() { bool check = m_Controls-> m_AdvancedCheckbox->isChecked(); m_Controls->m_QBallReconstructionMaxLLevelTextLabel_2->setVisible(check); m_Controls->m_QBallReconstructionMaxLLevelComboBox->setVisible(check); m_Controls->m_QBallReconstructionLambdaTextLabel_2->setVisible(check); m_Controls->m_QBallReconstructionLambdaLineEdit->setVisible(check); m_Controls->m_QBallReconstructionThresholdLabel_2->setVisible(check); m_Controls->m_QBallReconstructionThreasholdEdit->setVisible(check); m_Controls->m_OutputB0Image->setVisible(check); m_Controls->m_QBallReconstructionNumberThreadsLabel_2->setVisible(check); m_Controls->m_QBallReconstructionNumberThreadsSpinbox->setVisible(check); m_Controls->label_2->setVisible(check); //m_Controls->textLabel1_2->setVisible(check); //m_Controls->m_QBallReconstructionLambdaStepLineEdit->setVisible(check); //m_Controls->textLabel1_3->setVisible(check); m_Controls->frame_2->setVisible(check); } void QmitkQBallReconstructionView::Reconstruct(int method, int normalization) { if (m_CurrentSelection) { mitk::DataStorage::SetOfObjects::Pointer set = mitk::DataStorage::SetOfObjects::New(); int at = 0; for (IStructuredSelection::iterator i = m_CurrentSelection->Begin(); i != m_CurrentSelection->End(); ++i) { if (mitk::DataNodeObject::Pointer nodeObj = i->Cast()) { mitk::DataNode::Pointer node = nodeObj->GetDataNode(); if(QString("DiffusionImage").compare(node->GetData()->GetNameOfClass())==0) { set->InsertElement(at++, node); } } } if(method == 0) { NumericalQBallReconstruction(set, normalization); } else { #if BOOST_VERSION / 100000 > 0 #if BOOST_VERSION / 100 % 1000 > 34 if(method == 1) { AnalyticalQBallReconstruction(set, normalization); } #else std::cout << "ERROR: Boost 1.35 minimum required" << std::endl; QMessageBox::warning(NULL,"ERROR","Boost 1.35 minimum required"); #endif #else std::cout << "ERROR: Boost 1.35 minimum required" << std::endl; QMessageBox::warning(NULL,"ERROR","Boost 1.35 minimum required"); #endif } } } void QmitkQBallReconstructionView::NumericalQBallReconstruction (mitk::DataStorage::SetOfObjects::Pointer inImages, int normalization) { try { itk::TimeProbe clock; int nrFiles = inImages->size(); if (!nrFiles) return; QString status; mitk::ProgressBar::GetInstance()->AddStepsToDo(nrFiles); mitk::DataStorage::SetOfObjects::const_iterator itemiter( inImages->begin() ); mitk::DataStorage::SetOfObjects::const_iterator itemiterend( inImages->end() ); std::vector nodes; while ( itemiter != itemiterend ) // for all items { mitk::DiffusionImage* vols = static_cast*>( (*itemiter)->GetData()); std::string nodename; (*itemiter)->GetStringProperty("name", nodename); ++itemiter; // QBALL RECONSTRUCTION clock.Start(); MBI_INFO << "QBall reconstruction "; mitk::StatusBar::GetInstance()->DisplayText(status.sprintf( "QBall reconstruction for %s", nodename.c_str()).toAscii()); typedef itk::DiffusionQballReconstructionImageFilter QballReconstructionImageFilterType; QballReconstructionImageFilterType::Pointer filter = QballReconstructionImageFilterType::New(); filter->SetGradientImage( vols->GetDirections(), vols->GetVectorImage() ); filter->SetNumberOfThreads( m_Controls->m_QBallReconstructionNumberThreadsSpinbox->value() ); filter->SetBValue(vols->GetB_Value()); filter->SetThreshold( m_Controls->m_QBallReconstructionThreasholdEdit->text().toFloat() ); switch(normalization) { case 0: { filter->SetNormalizationMethod(QballReconstructionImageFilterType::QBR_STANDARD); break; } case 1: { filter->SetNormalizationMethod(QballReconstructionImageFilterType::QBR_B_ZERO_B_VALUE); break; } case 2: { filter->SetNormalizationMethod(QballReconstructionImageFilterType::QBR_B_ZERO); break; } case 3: { filter->SetNormalizationMethod(QballReconstructionImageFilterType::QBR_NONE); break; } default: { filter->SetNormalizationMethod(QballReconstructionImageFilterType::QBR_STANDARD); } } filter->Update(); clock.Stop(); MBI_DEBUG << "took " << clock.GetMeanTime() << "s." ; // ODFs TO DATATREE mitk::QBallImage::Pointer image = mitk::QBallImage::New(); image->InitializeByItk( filter->GetOutput() ); //image->SetImportVolume( filter->GetOutput()->GetBufferPointer(), 0, 0, mitk::Image::ImportMemoryManagementType::ManageMemory ); image->SetVolume( filter->GetOutput()->GetBufferPointer() ); mitk::DataNode::Pointer node=mitk::DataNode::New(); node->SetData( image ); QString newname; newname = newname.append(nodename.c_str()); newname = newname.append("_QN%1").arg(normalization); SetDefaultNodeProperties(node, newname.toStdString()); nodes.push_back(node); // B-Zero TO DATATREE if(m_Controls->m_OutputB0Image->isChecked()) { mitk::Image::Pointer image4 = mitk::Image::New(); image4->InitializeByItk( filter->GetBZeroImage().GetPointer() ); image4->SetVolume( filter->GetBZeroImage()->GetBufferPointer() ); mitk::DataNode::Pointer node4=mitk::DataNode::New(); node4->SetData( image4 ); node4->SetProperty( "name", mitk::StringProperty::New( QString(nodename.c_str()).append("_b0").toStdString()) ); nodes.push_back(node4); } mitk::ProgressBar::GetInstance()->Progress(); } std::vector::iterator nodeIt; for(nodeIt = nodes.begin(); nodeIt != nodes.end(); ++nodeIt) GetDefaultDataStorage()->Add(*nodeIt); mitk::StatusBar::GetInstance()->DisplayText(status.sprintf("Finished Processing %d Files", nrFiles).toAscii()); m_MultiWidget->RequestUpdate(); } catch (itk::ExceptionObject &ex) { MBI_INFO << ex ; return ; } } void QmitkQBallReconstructionView::AnalyticalQBallReconstruction( mitk::DataStorage::SetOfObjects::Pointer inImages, int normalization) { try { itk::TimeProbe clock; int nrFiles = inImages->size(); if (!nrFiles) return; std::vector lambdas; float minLambda = m_Controls->m_QBallReconstructionLambdaLineEdit->text().toFloat(); lambdas.push_back(minLambda); int nLambdas = lambdas.size(); QString status; mitk::ProgressBar::GetInstance()->AddStepsToDo(nrFiles*nLambdas); mitk::DataStorage::SetOfObjects::const_iterator itemiter( inImages->begin() ); mitk::DataStorage::SetOfObjects::const_iterator itemiterend( inImages->end() ); std::vector* nodes = new std::vector(); while ( itemiter != itemiterend ) // for all items { mitk::DiffusionImage* vols = static_cast*>( (*itemiter)->GetData()); std::string nodename; (*itemiter)->GetStringProperty("name",nodename); itemiter++; // QBALL RECONSTRUCTION clock.Start(); MBI_INFO << "QBall reconstruction "; mitk::StatusBar::GetInstance()->DisplayText(status.sprintf( "QBall reconstruction for %s", nodename.c_str()).toAscii()); for(int i=0; im_QBallReconstructionMaxLLevelComboBox->currentIndex()) { case 0: { TemplatedAnalyticalQBallReconstruction<2>(vols, currentLambda, nodename, nodes, normalization); break; } case 1: { TemplatedAnalyticalQBallReconstruction<4>(vols, currentLambda, nodename, nodes, normalization); break; } case 2: { TemplatedAnalyticalQBallReconstruction<6>(vols, currentLambda, nodename, nodes, normalization); break; } case 3: { TemplatedAnalyticalQBallReconstruction<8>(vols, currentLambda, nodename, nodes, normalization); break; } } clock.Stop(); MBI_DEBUG << "took " << clock.GetMeanTime() << "s." ; mitk::ProgressBar::GetInstance()->Progress(); } } std::vector::iterator nodeIt; for(nodeIt = nodes->begin(); nodeIt != nodes->end(); ++nodeIt) GetDefaultDataStorage()->Add(*nodeIt); m_MultiWidget->RequestUpdate(); mitk::StatusBar::GetInstance()->DisplayText(status.sprintf("Finished Processing %d Files", nrFiles).toAscii()); } catch (itk::ExceptionObject &ex) { MBI_INFO << ex ; return ; } } template void QmitkQBallReconstructionView::TemplatedAnalyticalQBallReconstruction( mitk::DiffusionImage* vols, float lambda, std::string nodename, std::vector* nodes, int normalization) { typedef itk::AnalyticalDiffusionQballReconstructionImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetGradientImage( vols->GetDirections(), vols->GetVectorImage() ); filter->SetNumberOfThreads( m_Controls->m_QBallReconstructionNumberThreadsSpinbox->value() ); filter->SetBValue(vols->GetB_Value()); filter->SetThreshold( m_Controls->m_QBallReconstructionThreasholdEdit->text().toFloat() ); filter->SetLambda(lambda); switch(normalization) { case 0: { filter->SetNormalizationMethod(FilterType::QBAR_STANDARD); break; } case 1: { filter->SetNormalizationMethod(FilterType::QBAR_B_ZERO_B_VALUE); break; } case 2: { filter->SetNormalizationMethod(FilterType::QBAR_B_ZERO); break; } case 3: { filter->SetNormalizationMethod(FilterType::QBAR_NONE); break; } case 4: { filter->SetNormalizationMethod(FilterType::QBAR_ADC_ONLY); break; } case 5: { filter->SetNormalizationMethod(FilterType::QBAR_RAW_SIGNAL); break; } case 6: { filter->SetNormalizationMethod(FilterType::QBAR_SOLID_ANGLE); break; } case 7: { filter->SetNormalizationMethod(FilterType::QBAR_NONNEG_SOLID_ANGLE); break; } default: { filter->SetNormalizationMethod(FilterType::QBAR_STANDARD); } } filter->Update(); // ODFs TO DATATREE mitk::QBallImage::Pointer image = mitk::QBallImage::New(); image->InitializeByItk( filter->GetOutput() ); image->SetVolume( filter->GetOutput()->GetBufferPointer() ); mitk::DataNode::Pointer node=mitk::DataNode::New(); node->SetData( image ); QString newname; newname = newname.append(nodename.c_str()); newname = newname.append("_QA%1").arg(normalization); SetDefaultNodeProperties(node, newname.toStdString()); nodes->push_back(node); + + +// mitk::Image::Pointer image5 = mitk::Image::New(); +// image5->InitializeByItk( filter->GetODFSumImage().GetPointer() ); +// image5->SetVolume( filter->GetODFSumImage()->GetBufferPointer() ); +// mitk::DataNode::Pointer node5=mitk::DataNode::New(); +// node5->SetData( image5 ); +// node5->SetProperty( "name", mitk::StringProperty::New( +// QString(nodename.c_str()).append("_ODF").toStdString()) ); +// nodes->push_back(node5); // B-Zero TO DATATREE if(m_Controls->m_OutputB0Image->isChecked()) { mitk::Image::Pointer image4 = mitk::Image::New(); image4->InitializeByItk( filter->GetBZeroImage().GetPointer() ); image4->SetVolume( filter->GetBZeroImage()->GetBufferPointer() ); mitk::DataNode::Pointer node4=mitk::DataNode::New(); node4->SetData( image4 ); node4->SetProperty( "name", mitk::StringProperty::New( QString(nodename.c_str()).append("_b0").toStdString()) ); nodes->push_back(node4); } } void QmitkQBallReconstructionView::SetDefaultNodeProperties(mitk::DataNode::Pointer node, std::string name) { node->SetProperty( "ShowMaxNumber", mitk::IntProperty::New( 500 ) ); node->SetProperty( "Scaling", mitk::FloatProperty::New( 1.0 ) ); node->SetProperty( "Normalization", mitk::OdfNormalizationMethodProperty::New()); node->SetProperty( "ScaleBy", mitk::OdfScaleByProperty::New()); node->SetProperty( "IndexParam1", mitk::FloatProperty::New(2)); node->SetProperty( "IndexParam2", mitk::FloatProperty::New(1)); node->SetProperty( "visible", mitk::BoolProperty::New( true ) ); node->SetProperty( "VisibleOdfs", mitk::BoolProperty::New( false ) ); node->SetProperty ("layer", mitk::IntProperty::New(100)); node->SetProperty( "DoRefresh", mitk::BoolProperty::New( true ) ); //node->SetProperty( "opacity", mitk::FloatProperty::New(1.0f) ); node->SetProperty( "name", mitk::StringProperty::New(name) ); } //node->SetProperty( "volumerendering", mitk::BoolProperty::New( false ) ); //node->SetProperty( "use color", mitk::BoolProperty::New( true ) ); //node->SetProperty( "texture interpolation", mitk::BoolProperty::New( true ) ); //node->SetProperty( "reslice interpolation", mitk::VtkResliceInterpolationProperty::New() ); //node->SetProperty( "layer", mitk::IntProperty::New(0)); //node->SetProperty( "in plane resample extent by geometry", mitk::BoolProperty::New( false ) ); //node->SetOpacity(1.0f); //node->SetColor(1.0,1.0,1.0); //node->SetVisibility(true); //node->SetProperty( "IsQBallVolume", mitk::BoolProperty::New( true ) ); //mitk::LevelWindowProperty::Pointer levWinProp = mitk::LevelWindowProperty::New(); //mitk::LevelWindow levelwindow; //// levelwindow.SetAuto( image ); //levWinProp->SetLevelWindow( levelwindow ); //node->GetPropertyList()->SetProperty( "levelwindow", levWinProp ); //// add a default rainbow lookup table for color mapping //if(!node->GetProperty("LookupTable")) //{ // mitk::LookupTable::Pointer mitkLut = mitk::LookupTable::New(); // vtkLookupTable* vtkLut = mitkLut->GetVtkLookupTable(); // vtkLut->SetHueRange(0.6667, 0.0); // vtkLut->SetTableRange(0.0, 20.0); // vtkLut->Build(); // mitk::LookupTableProperty::Pointer mitkLutProp = mitk::LookupTableProperty::New(); // mitkLutProp->SetLookupTable(mitkLut); // node->SetProperty( "LookupTable", mitkLutProp ); //} //if(!node->GetProperty("binary")) // node->SetProperty( "binary", mitk::BoolProperty::New( false ) ); //// add a default transfer function //mitk::TransferFunction::Pointer tf = mitk::TransferFunction::New(); //node->SetProperty ( "TransferFunction", mitk::TransferFunctionProperty::New ( tf.GetPointer() ) ); //// set foldername as string property //mitk::StringProperty::Pointer nameProp = mitk::StringProperty::New( name ); //node->SetProperty( "name", nameProp ); diff --git a/Modules/Bundles/org.mitk.gui.qt.diffusionimaging/src/internal/QmitkTensorReconstructionView.cpp b/Modules/Bundles/org.mitk.gui.qt.diffusionimaging/src/internal/QmitkTensorReconstructionView.cpp index 0f225e9755..81476a0f1a 100644 --- a/Modules/Bundles/org.mitk.gui.qt.diffusionimaging/src/internal/QmitkTensorReconstructionView.cpp +++ b/Modules/Bundles/org.mitk.gui.qt.diffusionimaging/src/internal/QmitkTensorReconstructionView.cpp @@ -1,795 +1,796 @@ /*========================================================================= Program: Medical Imaging & Interaction Toolkit Module: $RCSfile$ Language: C++ Date: $Date: 2009-05-28 17:19:30 +0200 (Do, 28 Mai 2009) $ Version: $Revision: 17495 $ Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. See MITKCopyright.txt or http://www.mitk.org/copyright.html for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "QmitkTensorReconstructionView.h" #include "mitkDiffusionImagingConfigure.h" // qt includes #include // itk includes #include "itkTimeProbe.h" //#include "itkTensor.h" // mitk includes #include "mitkProgressBar.h" #include "mitkStatusBar.h" #include "mitkNodePredicateDataType.h" #include "QmitkDataStorageComboBox.h" #include "QmitkStdMultiWidget.h" #include "mitkTeemDiffusionTensor3DReconstructionImageFilter.h" #include "itkDiffusionTensor3DReconstructionImageFilter.h" #include "itkTensorImageToDiffusionImageFilter.h" #include "itkPointShell.h" #include "itkVector.h" #include "mitkTensorImage.h" #include "mitkProperties.h" #include "mitkDataNodeObject.h" #include "mitkOdfNormalizationMethodProperty.h" #include "mitkOdfScaleByProperty.h" #include "mitkDiffusionImageMapper.h" #include "berryIStructuredSelection.h" #include "berryIWorkbenchWindow.h" #include "berryISelectionService.h" const std::string QmitkTensorReconstructionView::VIEW_ID = "org.mitk.views.tensorreconstruction"; #define DI_INFO MITK_INFO("DiffusionImaging") typedef float TTensorPixelType; using namespace berry; struct TrSelListener : ISelectionListener { berryObjectMacro(TrSelListener); TrSelListener(QmitkTensorReconstructionView* view) { m_View = view; } void DoSelectionChanged(ISelection::ConstPointer selection) { // if(!m_View->IsVisible()) // return; // save current selection in member variable m_View->m_CurrentSelection = selection.Cast(); // do something with the selected items if(m_View->m_CurrentSelection) { bool foundDwiVolume = false; bool foundTensorVolume = false; // iterate selection for (IStructuredSelection::iterator i = m_View->m_CurrentSelection->Begin(); i != m_View->m_CurrentSelection->End(); ++i) { // extract datatree node if (mitk::DataNodeObject::Pointer nodeObj = i->Cast()) { mitk::DataNode::Pointer node = nodeObj->GetDataNode(); // only look at interesting types if(QString("DiffusionImage").compare(node->GetData()->GetNameOfClass())==0) { foundDwiVolume = true; } // only look at interesting types if(QString("TensorImage").compare(node->GetData()->GetNameOfClass())==0) { foundTensorVolume = true; } } } m_View->m_Controls->m_ItkReconstruction->setEnabled(foundDwiVolume); m_View->m_Controls->m_TeemReconstruction->setEnabled(foundDwiVolume); m_View->m_Controls->m_TensorsToDWIButton->setEnabled(foundTensorVolume); } } void SelectionChanged(IWorkbenchPart::Pointer part, ISelection::ConstPointer selection) { // check, if selection comes from datamanager if (part) { QString partname(part->GetPartName().c_str()); if(partname.compare("Datamanager")==0) { // apply selection DoSelectionChanged(selection); } } } QmitkTensorReconstructionView* m_View; }; QmitkTensorReconstructionView::QmitkTensorReconstructionView() : QmitkFunctionality(), m_Controls(NULL), m_MultiWidget(NULL) { } QmitkTensorReconstructionView::~QmitkTensorReconstructionView() { this->GetSite()->GetWorkbenchWindow()->GetSelectionService()->RemovePostSelectionListener(/*"org.mitk.views.datamanager",*/ m_SelListener); } void QmitkTensorReconstructionView::CreateQtPartControl(QWidget *parent) { if (!m_Controls) { // create GUI widgets m_Controls = new Ui::QmitkTensorReconstructionViewControls; m_Controls->setupUi(parent); this->CreateConnections(); m_Controls->m_TensorReconstructionNumberThreadsSpinbox->setValue(8); QStringList items; items << "LLS (Linear Least Squares)" << "MLE (Maximum Likelihood)" << "NLS (Nonlinear Least Squares)" << "WLS (Weighted Least Squares)"; m_Controls->m_TensorEstimationTeemEstimationMethodCombo->addItems(items); m_Controls->m_TensorEstimationTeemEstimationMethodCombo->setCurrentIndex(0); m_Controls->m_TensorEstimationManualThreashold->setChecked(false); m_Controls->m_TensorEstimationTeemSigmaEdit->setText("NaN"); m_Controls->m_TensorEstimationTeemNumItsSpin->setValue(1); m_Controls->m_TensorEstimationTeemFuzzyEdit->setText("0.0"); m_Controls->m_TensorEstimationTeemMinValEdit->setText("1.0"); m_Controls->m_TensorEstimationTeemNumItsLabel_2->setEnabled(true); m_Controls->m_TensorEstimationTeemNumItsSpin->setEnabled(true); m_Controls->m_TensorsToDWIBValueEdit->setText("1000"); Advanced1CheckboxClicked(); Advanced2CheckboxClicked(); TeemCheckboxClicked(); #ifndef DIFFUSION_IMAGING_EXTENDED m_Controls->m_TeemToggle->setVisible(false); #endif // define data type for combobox //m_Controls->m_ImageSelector->SetDataStorage( this->GetDefaultDataStorage() ); //m_Controls->m_ImageSelector->SetPredicate( mitk::NodePredicateDataType::New("DiffusionImage") ); } m_SelListener = berry::ISelectionListener::Pointer(new TrSelListener(this)); this->GetSite()->GetWorkbenchWindow()->GetSelectionService()->AddPostSelectionListener(/*"org.mitk.views.datamanager",*/ m_SelListener); berry::ISelection::ConstPointer sel( this->GetSite()->GetWorkbenchWindow()->GetSelectionService()->GetSelection("org.mitk.views.datamanager")); m_CurrentSelection = sel.Cast(); m_SelListener.Cast()->DoSelectionChanged(sel); } void QmitkTensorReconstructionView::StdMultiWidgetAvailable (QmitkStdMultiWidget &stdMultiWidget) { berry::ISelection::ConstPointer sel( this->GetSite()->GetWorkbenchWindow()->GetSelectionService()->GetSelection("org.mitk.views.datamanager")); m_CurrentSelection = sel.Cast(); m_SelListener.Cast()->DoSelectionChanged(sel); m_MultiWidget = &stdMultiWidget; } void QmitkTensorReconstructionView::StdMultiWidgetNotAvailable() { m_MultiWidget = NULL; } void QmitkTensorReconstructionView::CreateConnections() { if ( m_Controls ) { connect( (QObject*)(m_Controls->m_TeemToggle), SIGNAL(clicked()), this, SLOT(TeemCheckboxClicked()) ); connect( (QObject*)(m_Controls->m_ItkReconstruction), SIGNAL(clicked()), this, SLOT(ItkReconstruction()) ); connect( (QObject*)(m_Controls->m_TeemReconstruction), SIGNAL(clicked()), this, SLOT(TeemReconstruction()) ); connect( (QObject*)(m_Controls->m_TensorEstimationTeemEstimationMethodCombo), SIGNAL(currentIndexChanged(int)), this, SLOT(MethodChoosen(int)) ); connect( (QObject*)(m_Controls->m_Advanced1), SIGNAL(clicked()), this, SLOT(Advanced1CheckboxClicked()) ); connect( (QObject*)(m_Controls->m_Advanced2), SIGNAL(clicked()), this, SLOT(Advanced2CheckboxClicked()) ); connect( (QObject*)(m_Controls->m_TensorEstimationManualThreashold), SIGNAL(clicked()), this, SLOT(ManualThresholdClicked()) ); connect( (QObject*)(m_Controls->m_TensorsToDWIButton), SIGNAL(clicked()), this, SLOT(TensorsToDWI()) ); } } void QmitkTensorReconstructionView::TeemCheckboxClicked() { m_Controls->groupBox_3->setVisible(m_Controls-> m_TeemToggle->isChecked()); } void QmitkTensorReconstructionView::Advanced1CheckboxClicked() { bool check = m_Controls-> m_Advanced1->isChecked(); m_Controls->frame->setVisible(check); } void QmitkTensorReconstructionView::Advanced2CheckboxClicked() { bool check = m_Controls-> m_Advanced2->isChecked(); m_Controls->frame_2->setVisible(check); } void QmitkTensorReconstructionView::ManualThresholdClicked() { m_Controls->m_TensorReconstructionThreasholdEdit_2->setEnabled( m_Controls->m_TensorEstimationManualThreashold->isChecked()); } void QmitkTensorReconstructionView::Activated() { QmitkFunctionality::Activated(); } void QmitkTensorReconstructionView::Deactivated() { QmitkFunctionality::Deactivated(); } void QmitkTensorReconstructionView::MethodChoosen(int method) { m_Controls->m_TensorEstimationTeemNumItsLabel_2->setEnabled(method==3); m_Controls->m_TensorEstimationTeemNumItsSpin->setEnabled(method==3); } void QmitkTensorReconstructionView::ItkReconstruction() { Reconstruct(0); } void QmitkTensorReconstructionView::TeemReconstruction() { Reconstruct(1); } void QmitkTensorReconstructionView::Reconstruct(int method) { if (m_CurrentSelection) { mitk::DataStorage::SetOfObjects::Pointer set = mitk::DataStorage::SetOfObjects::New(); int at = 0; for (IStructuredSelection::iterator i = m_CurrentSelection->Begin(); i != m_CurrentSelection->End(); ++i) { if (mitk::DataNodeObject::Pointer nodeObj = i->Cast()) { mitk::DataNode::Pointer node = nodeObj->GetDataNode(); if(QString("DiffusionImage").compare(node->GetData()->GetNameOfClass())==0) { set->InsertElement(at++, node); } } } if(method == 0) { ItkTensorReconstruction(set); } if(method == 1) { TeemTensorReconstruction(set); } } } void QmitkTensorReconstructionView::ItkTensorReconstruction (mitk::DataStorage::SetOfObjects::Pointer inImages) { try { itk::TimeProbe clock; int nrFiles = inImages->size(); if (!nrFiles) return; QString status; mitk::ProgressBar::GetInstance()->AddStepsToDo(nrFiles); mitk::DataStorage::SetOfObjects::const_iterator itemiter( inImages->begin() ); mitk::DataStorage::SetOfObjects::const_iterator itemiterend( inImages->end() ); std::vector nodes; while ( itemiter != itemiterend ) // for all items { mitk::DiffusionImage* vols = static_cast*>( (*itemiter)->GetData()); std::string nodename; (*itemiter)->GetStringProperty("name", nodename); ++itemiter; // TENSOR RECONSTRUCTION clock.Start(); MBI_INFO << "Tensor reconstruction "; mitk::StatusBar::GetInstance()->DisplayText(status.sprintf( "Tensor reconstruction for %s", nodename.c_str()).toAscii()); typedef itk::DiffusionTensor3DReconstructionImageFilter< DiffusionPixelType, DiffusionPixelType, TTensorPixelType > TensorReconstructionImageFilterType; TensorReconstructionImageFilterType::Pointer tensorReconstructionFilter = TensorReconstructionImageFilterType::New(); tensorReconstructionFilter->SetGradientImage( vols->GetDirections(), vols->GetVectorImage() ); tensorReconstructionFilter->SetNumberOfThreads( m_Controls->m_TensorReconstructionNumberThreadsSpinbox->value() ); tensorReconstructionFilter->SetBValue(vols->GetB_Value()); tensorReconstructionFilter->SetThreshold( m_Controls->m_TensorReconstructionThreasholdEdit->text().toFloat() ); tensorReconstructionFilter->Update(); clock.Stop(); MBI_DEBUG << "took " << clock.GetMeanTime() << "s."; // TENSORS TO DATATREE mitk::TensorImage::Pointer image = mitk::TensorImage::New(); typedef itk::Image, 3> TensorImageType; TensorImageType::Pointer tensorImage; tensorImage = tensorReconstructionFilter->GetOutput(); // Check the tensor for negative eigenvalues if(m_Controls->m_CheckNegativeEigenvalues->isChecked()) { typedef itk::ImageRegionIterator TensorImageIteratorType; TensorImageIteratorType tensorIt(tensorImage, tensorImage->GetRequestedRegion()); tensorIt.GoToBegin(); while(!tensorIt.IsAtEnd()) { typedef itk::DiffusionTensor3D TensorType; //typedef itk::Tensor TensorType2; TensorType tensor = tensorIt.Get(); // TensorType2 tensor2; /* for(int i=0; i SymEigenSystemType; SymEigenSystemType eig (tensor2.GetVnlMatrix()); for(unsigned int i=0; iInitializeByItk( tensorImage.GetPointer() ); image->SetVolume( tensorReconstructionFilter->GetOutput()->GetBufferPointer() ); mitk::DataNode::Pointer node=mitk::DataNode::New(); node->SetData( image ); QString newname; newname = newname.append(nodename.c_str()); newname = newname.append("_dti"); SetDefaultNodeProperties(node, newname.toStdString()); nodes.push_back(node); mitk::ProgressBar::GetInstance()->Progress(); } std::vector::iterator nodeIt; for(nodeIt = nodes.begin(); nodeIt != nodes.end(); ++nodeIt) GetDefaultDataStorage()->Add(*nodeIt); mitk::StatusBar::GetInstance()->DisplayText(status.sprintf("Finished Processing %d Files", nrFiles).toAscii()); m_MultiWidget->RequestUpdate(); } catch (itk::ExceptionObject &ex) { MBI_INFO << ex ; return ; } } void QmitkTensorReconstructionView::TeemTensorReconstruction (mitk::DataStorage::SetOfObjects::Pointer inImages) { try { itk::TimeProbe clock; int nrFiles = inImages->size(); if (!nrFiles) return; QString status; mitk::ProgressBar::GetInstance()->AddStepsToDo(nrFiles); mitk::DataStorage::SetOfObjects::const_iterator itemiter( inImages->begin() ); mitk::DataStorage::SetOfObjects::const_iterator itemiterend( inImages->end() ); std::vector nodes; while ( itemiter != itemiterend ) // for all items { mitk::DiffusionImage* vols = static_cast*>( (*itemiter)->GetData()); std::string nodename; (*itemiter)->GetStringProperty("name", nodename); ++itemiter; // TENSOR RECONSTRUCTION clock.Start(); MBI_INFO << "Teem Tensor reconstruction "; mitk::StatusBar::GetInstance()->DisplayText(status.sprintf( "Teem Tensor reconstruction for %s", nodename.c_str()).toAscii()); typedef mitk::TeemDiffusionTensor3DReconstructionImageFilter< DiffusionPixelType, TTensorPixelType > TensorReconstructionImageFilterType; TensorReconstructionImageFilterType::Pointer tensorReconstructionFilter = TensorReconstructionImageFilterType::New(); tensorReconstructionFilter->SetInput( vols ); if(!m_Controls->m_TensorEstimationTeemSigmaEdit->text().contains(QString("NaN"))) tensorReconstructionFilter->SetSigma( m_Controls->m_TensorEstimationTeemSigmaEdit->text().toFloat() ); switch(m_Controls->m_TensorEstimationTeemEstimationMethodCombo->currentIndex()) { // items << "LLS (Linear Least Squares)" //<< "MLE (Maximum Likelihood)" //<< "NLS (Nonlinear Least Squares)" //<< "WLS (Weighted Least Squares)"; case 0: tensorReconstructionFilter->SetEstimationMethod(mitk::TeemTensorEstimationMethodsLLS); break; case 1: tensorReconstructionFilter->SetEstimationMethod(mitk::TeemTensorEstimationMethodsMLE); break; case 2: tensorReconstructionFilter->SetEstimationMethod(mitk::TeemTensorEstimationMethodsNLS); break; case 3: tensorReconstructionFilter->SetEstimationMethod(mitk::TeemTensorEstimationMethodsWLS); break; default: tensorReconstructionFilter->SetEstimationMethod(mitk::TeemTensorEstimationMethodsLLS); } tensorReconstructionFilter->SetNumIterations( m_Controls->m_TensorEstimationTeemNumItsSpin->value() ); if(m_Controls->m_TensorEstimationManualThreashold->isChecked()) tensorReconstructionFilter->SetConfidenceThreshold( m_Controls->m_TensorReconstructionThreasholdEdit_2->text().toDouble() ); tensorReconstructionFilter->SetConfidenceFuzzyness( m_Controls->m_TensorEstimationTeemFuzzyEdit->text().toFloat() ); tensorReconstructionFilter->SetMinPlausibleValue( m_Controls->m_TensorEstimationTeemMinValEdit->text().toDouble() ); tensorReconstructionFilter->Update(); clock.Stop(); MBI_DEBUG << "took " << clock.GetMeanTime() << "s." ; // TENSORS TO DATATREE mitk::DataNode::Pointer node2=mitk::DataNode::New(); node2->SetData( tensorReconstructionFilter->GetOutputItk() ); QString newname; newname = newname.append(nodename.c_str()); newname = newname.append("_dtix"); SetDefaultNodeProperties(node2, newname.toStdString()); nodes.push_back(node2); mitk::ProgressBar::GetInstance()->Progress(); } std::vector::iterator nodeIt; for(nodeIt = nodes.begin(); nodeIt != nodes.end(); ++nodeIt) GetDefaultDataStorage()->Add(*nodeIt); mitk::StatusBar::GetInstance()->DisplayText(status.sprintf("Finished Processing %d Files", nrFiles).toAscii()); m_MultiWidget->RequestUpdate(); } catch (itk::ExceptionObject &ex) { MBI_INFO << ex ; return ; } } void QmitkTensorReconstructionView::SetDefaultNodeProperties(mitk::DataNode::Pointer node, std::string name) { node->SetProperty( "ShowMaxNumber", mitk::IntProperty::New( 500 ) ); node->SetProperty( "Scaling", mitk::FloatProperty::New( 1.0 ) ); node->SetProperty( "Normalization", mitk::OdfNormalizationMethodProperty::New()); node->SetProperty( "ScaleBy", mitk::OdfScaleByProperty::New()); node->SetProperty( "IndexParam1", mitk::FloatProperty::New(2)); node->SetProperty( "IndexParam2", mitk::FloatProperty::New(1)); node->SetProperty( "visible", mitk::BoolProperty::New( true ) ); node->SetProperty( "VisibleOdfs", mitk::BoolProperty::New( false ) ); node->SetProperty ("layer", mitk::IntProperty::New(100)); node->SetProperty( "DoRefresh", mitk::BoolProperty::New( true ) ); //node->SetProperty( "opacity", mitk::FloatProperty::New(1.0f) ); node->SetProperty( "name", mitk::StringProperty::New(name) ); } //node->SetProperty( "volumerendering", mitk::BoolProperty::New( false ) ); //node->SetProperty( "use color", mitk::BoolProperty::New( true ) ); //node->SetProperty( "texture interpolation", mitk::BoolProperty::New( true ) ); //node->SetProperty( "reslice interpolation", mitk::VtkResliceInterpolationProperty::New() ); //node->SetProperty( "layer", mitk::IntProperty::New(0)); //node->SetProperty( "in plane resample extent by geometry", mitk::BoolProperty::New( false ) ); //node->SetOpacity(1.0f); //node->SetColor(1.0,1.0,1.0); //node->SetVisibility(true); //node->SetProperty( "IsTensorVolume", mitk::BoolProperty::New( true ) ); //mitk::LevelWindowProperty::Pointer levWinProp = mitk::LevelWindowProperty::New(); //mitk::LevelWindow levelwindow; //// levelwindow.SetAuto( image ); //levWinProp->SetLevelWindow( levelwindow ); //node->GetPropertyList()->SetProperty( "levelwindow", levWinProp ); //// add a default rainbow lookup table for color mapping //if(!node->GetProperty("LookupTable")) //{ // mitk::LookupTable::Pointer mitkLut = mitk::LookupTable::New(); // vtkLookupTable* vtkLut = mitkLut->GetVtkLookupTable(); // vtkLut->SetHueRange(0.6667, 0.0); // vtkLut->SetTableRange(0.0, 20.0); // vtkLut->Build(); // mitk::LookupTableProperty::Pointer mitkLutProp = mitk::LookupTableProperty::New(); // mitkLutProp->SetLookupTable(mitkLut); // node->SetProperty( "LookupTable", mitkLutProp ); //} //if(!node->GetProperty("binary")) // node->SetProperty( "binary", mitk::BoolProperty::New( false ) ); //// add a default transfer function //mitk::TransferFunction::Pointer tf = mitk::TransferFunction::New(); //node->SetProperty ( "TransferFunction", mitk::TransferFunctionProperty::New ( tf.GetPointer() ) ); //// set foldername as string property //mitk::StringProperty::Pointer nameProp = mitk::StringProperty::New( name ); //node->SetProperty( "name", nameProp ); void QmitkTensorReconstructionView::TensorsToDWI() { if (m_CurrentSelection) { mitk::DataStorage::SetOfObjects::Pointer set = mitk::DataStorage::SetOfObjects::New(); int at = 0; for (IStructuredSelection::iterator i = m_CurrentSelection->Begin(); i != m_CurrentSelection->End(); ++i) { if (mitk::DataNodeObject::Pointer nodeObj = i->Cast()) { mitk::DataNode::Pointer node = nodeObj->GetDataNode(); if(QString("TensorImage").compare(node->GetData()->GetNameOfClass())==0) { set->InsertElement(at++, node); } } } DoTensorsToDWI(set); } } template std::vector > QmitkTensorReconstructionView::MakeGradientList() { std::vector > retval; vnl_matrix_fixed* U = itk::PointShell >::DistributePointShell(); for(int i=0; i v; v[0] = U->get(0,i); v[1] = U->get(1,i); v[2] = U->get(2,i); retval.push_back(v); } return retval; } void QmitkTensorReconstructionView::DoTensorsToDWI (mitk::DataStorage::SetOfObjects::Pointer inImages) { try { itk::TimeProbe clock; int nrFiles = inImages->size(); if (!nrFiles) return; QString status; mitk::ProgressBar::GetInstance()->AddStepsToDo(nrFiles); mitk::DataStorage::SetOfObjects::const_iterator itemiter( inImages->begin() ); mitk::DataStorage::SetOfObjects::const_iterator itemiterend( inImages->end() ); std::vector nodes; while ( itemiter != itemiterend ) // for all items { std::string nodename; (*itemiter)->GetStringProperty("name", nodename); mitk::TensorImage* vol = static_cast((*itemiter)->GetData()); ++itemiter; typedef float TTensorPixelType; typedef itk::DiffusionTensor3D< TTensorPixelType > TensorPixelType; typedef itk::Image< TensorPixelType, 3 > TensorImageType; TensorImageType::Pointer itkvol = TensorImageType::New(); mitk::CastToItkImage(vol, itkvol); typedef itk::TensorImageToDiffusionImageFilter< TTensorPixelType, DiffusionPixelType > FilterType; FilterType::GradientListType gradientList; switch(m_Controls->m_TensorsToDWINumDirsSelect->currentIndex()) { case 0: gradientList = MakeGradientList<12>(); break; case 1: gradientList = MakeGradientList<42>(); break; case 2: gradientList = MakeGradientList<92>(); break; case 3: gradientList = MakeGradientList<162>(); break; case 4: gradientList = MakeGradientList<252>(); break; case 5: gradientList = MakeGradientList<362>(); break; case 6: gradientList = MakeGradientList<492>(); break; case 7: gradientList = MakeGradientList<642>(); break; case 8: gradientList = MakeGradientList<812>(); break; case 9: gradientList = MakeGradientList<1002>(); break; default: gradientList = MakeGradientList<92>(); } double bVal = m_Controls->m_TensorsToDWIBValueEdit->text().toDouble(); // DWI ESTIMATION clock.Start(); MBI_INFO << "DWI Estimation "; mitk::StatusBar::GetInstance()->DisplayText(status.sprintf( "DWI Estimation for %s", nodename.c_str()).toAscii()); FilterType::Pointer filter = FilterType::New(); filter->SetInput( itkvol ); filter->SetBValue(bVal); filter->SetGradientList(gradientList); filter->SetNumberOfThreads(1); filter->Update(); clock.Stop(); MBI_DEBUG << "took " << clock.GetMeanTime() << "s."; itk::Vector v; v[0] = 0; v[1] = 0; v[2] = 0; gradientList.push_back(v); // TENSORS TO DATATREE mitk::DiffusionImage::Pointer image = mitk::DiffusionImage::New(); image->SetVectorImage( filter->GetOutput() ); image->SetB_Value(bVal); image->SetDirections(gradientList); + image->SetOriginalDirections(gradientList); image->InitializeFromVectorImage(); mitk::DataNode::Pointer node=mitk::DataNode::New(); node->SetData( image ); mitk::DiffusionImageMapper::SetDefaultProperties(node); QString newname; newname = newname.append(nodename.c_str()); newname = newname.append("_dwi"); node->SetName(newname.toAscii()); nodes.push_back(node); mitk::ProgressBar::GetInstance()->Progress(); } std::vector::iterator nodeIt; for(nodeIt = nodes.begin(); nodeIt != nodes.end(); ++nodeIt) GetDefaultDataStorage()->Add(*nodeIt); mitk::StatusBar::GetInstance()->DisplayText(status.sprintf("Finished Processing %d Files", nrFiles).toAscii()); m_MultiWidget->RequestUpdate(); } catch (itk::ExceptionObject &ex) { MBI_INFO << ex ; return ; } -} \ No newline at end of file +} diff --git a/Modules/DiffusionImaging/Reconstruction/itkAnalyticalDiffusionQballReconstructionImageFilter.cpp b/Modules/DiffusionImaging/Reconstruction/itkAnalyticalDiffusionQballReconstructionImageFilter.cpp index 9141507c10..511cd4a790 100644 --- a/Modules/DiffusionImaging/Reconstruction/itkAnalyticalDiffusionQballReconstructionImageFilter.cpp +++ b/Modules/DiffusionImaging/Reconstruction/itkAnalyticalDiffusionQballReconstructionImageFilter.cpp @@ -1,853 +1,884 @@ /*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkDiffusionTensor3DReconstructionImageFilter.txx,v $ Language: C++ Date: $Date: 2006-07-19 15:11:41 $ Version: $Revision: 1.11 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkAnalyticalDiffusionQballReconstructionImageFilter_cpp #define __itkAnalyticalDiffusionQballReconstructionImageFilter_cpp #include "itkAnalyticalDiffusionQballReconstructionImageFilter.h" #include "itkImageRegionConstIterator.h" #include "itkImageRegionConstIteratorWithIndex.h" #include "itkImageRegionIterator.h" #include "itkArray.h" #include "vnl/vnl_vector.h" #include #if BOOST_VERSION / 100000 > 0 #if BOOST_VERSION / 100 % 1000 > 34 #include #endif #endif #include "itkPointShell.h" namespace itk { #define QBALL_ANAL_RECON_PI 3.14159265358979323846 template< class T, class TG, class TO, int L, int NODF> AnalyticalDiffusionQballReconstructionImageFilter ::AnalyticalDiffusionQballReconstructionImageFilter() : m_GradientDirectionContainer(NULL), m_NumberOfGradientDirections(0), m_NumberOfBaselineImages(1), m_Threshold(NumericTraits< ReferencePixelType >::NonpositiveMin()), m_BValue(1.0), m_Lambda(0.0), m_DirectionsDuplicated(false) { // At least 1 inputs is necessary for a vector image. // For images added one at a time we need at least six this->SetNumberOfRequiredInputs( 1 ); } template< class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections> typename itk::AnalyticalDiffusionQballReconstructionImageFilter< TReferenceImagePixelType,TGradientImagePixelType,TOdfPixelType, NOrderL,NrOdfDirections>::OdfPixelType itk::AnalyticalDiffusionQballReconstructionImageFilter ::Normalize( OdfPixelType odf, typename NumericTraits::AccumulateType b0 ) { switch( m_NormalizationMethod ) { case QBAR_STANDARD: { TOdfPixelType sum = 0; for(int i=0; i0) + odf /= sum; return odf; break; } case QBAR_B_ZERO_B_VALUE: { for(int i=0; i0) + odf /= sum; + break; } case QBAR_NONNEG_SOLID_ANGLE: { break; } } return odf; } template< class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections> vnl_vector itk::AnalyticalDiffusionQballReconstructionImageFilter ::PreNormalize( vnl_vector vec, typename NumericTraits::AccumulateType b0 ) { switch( m_NormalizationMethod ) { case QBAR_STANDARD: { return vec; break; } case QBAR_B_ZERO_B_VALUE: { int n = vec.size(); for(int i=0; i= b0) - { - meas = b0 - 0.0001; - } - vec[i] = log(-log(meas/b0)); + double meas = (double)vec[i]; + if (meas==0) + meas = 0.001; + if (b0f==0) + b0f = 0.01; + if(meas >= b0f) + meas = b0f - 0.001; + vec[i] = log(-log(meas/b0f)); } return vec; break; } } return vec; } template< class T, class TG, class TO, int L, int NODF> void AnalyticalDiffusionQballReconstructionImageFilter ::BeforeThreadedGenerateData() { // If we have more than 2 inputs, then each input, except the first is a // gradient image. The number of gradient images must match the number of // gradient directions. //const unsigned int numberOfInputs = this->GetNumberOfInputs(); // There need to be at least 6 gradient directions to be able to compute the // tensor basis if( m_NumberOfGradientDirections < 6 ) { itkExceptionMacro( << "At least 6 gradient directions are required" ); } // Input must be an itk::VectorImage. std::string gradientImageClassName( this->ProcessObject::GetInput(0)->GetNameOfClass()); if ( strcmp(gradientImageClassName.c_str(),"VectorImage") != 0 ) { itkExceptionMacro( << "There is only one Gradient image. I expect that to be a VectorImage. " << "But its of type: " << gradientImageClassName ); } this->ComputeReconstructionMatrix(); m_BZeroImage = BZeroImageType::New(); typename GradientImagesType::Pointer img = static_cast< GradientImagesType * >( this->ProcessObject::GetInput(0) ); m_BZeroImage->SetSpacing( img->GetSpacing() ); // Set the image spacing m_BZeroImage->SetOrigin( img->GetOrigin() ); // Set the image origin m_BZeroImage->SetDirection( img->GetDirection() ); // Set the image direction m_BZeroImage->SetLargestPossibleRegion( img->GetLargestPossibleRegion()); m_BZeroImage->SetBufferedRegion( img->GetLargestPossibleRegion() ); m_BZeroImage->Allocate(); + m_ODFSumImage = BZeroImageType::New(); + m_ODFSumImage->SetSpacing( img->GetSpacing() ); // Set the image spacing + m_ODFSumImage->SetOrigin( img->GetOrigin() ); // Set the image origin + m_ODFSumImage->SetDirection( img->GetDirection() ); // Set the image direction + m_ODFSumImage->SetLargestPossibleRegion( img->GetLargestPossibleRegion()); + m_ODFSumImage->SetBufferedRegion( img->GetLargestPossibleRegion() ); + m_ODFSumImage->Allocate(); + if(m_NormalizationMethod == QBAR_SOLID_ANGLE || m_NormalizationMethod == QBAR_NONNEG_SOLID_ANGLE) { m_Lambda = 0.0; } } template< class T, class TG, class TO, int L, int NODF> void AnalyticalDiffusionQballReconstructionImageFilter ::ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int ) { typename OutputImageType::Pointer outputImage = static_cast< OutputImageType * >(this->ProcessObject::GetOutput(0)); ImageRegionIterator< OutputImageType > oit(outputImage, outputRegionForThread); oit.GoToBegin(); ImageRegionIterator< BZeroImageType > oit2(m_BZeroImage, outputRegionForThread); oit2.GoToBegin(); + ImageRegionIterator< BlaImage > oit3(m_ODFSumImage, outputRegionForThread); + oit3.GoToBegin(); + typedef ImageRegionConstIterator< GradientImagesType > GradientIteratorType; typedef typename GradientImagesType::PixelType GradientVectorType; typename GradientImagesType::Pointer gradientImagePointer = NULL; // Would have liked a dynamic_cast here, but seems SGI doesn't like it // The enum will ensure that an inappropriate cast is not done gradientImagePointer = static_cast< GradientImagesType * >( this->ProcessObject::GetInput(0) ); GradientIteratorType git(gradientImagePointer, outputRegionForThread ); git.GoToBegin(); // Compute the indicies of the baseline images and gradient images std::vector baselineind; // contains the indicies of // the baseline images std::vector gradientind; // contains the indicies of // the gradient images for(GradientDirectionContainerType::ConstIterator gdcit = this->m_GradientDirectionContainer->Begin(); gdcit != this->m_GradientDirectionContainer->End(); ++gdcit) { if(gdcit.Value().one_norm() <= 0.0) { baselineind.push_back(gdcit.Index()); } else { gradientind.push_back(gdcit.Index()); } } if( m_DirectionsDuplicated ) { int gradIndSize = gradientind.size(); for(int i=0; i::AccumulateType b0 = NumericTraits::Zero; // Average the baseline image pixels for(unsigned int i = 0; i < baselineind.size(); ++i) { b0 += b[baselineind[i]]; } b0 /= this->m_NumberOfBaselineImages; OdfPixelType odf(0.0); vnl_vector B(m_NumberOfGradientDirections); if( (b0 != 0) && (b0 >= m_Threshold) ) { for( unsigned int i = 0; i< m_NumberOfGradientDirections; i++ ) { B[i] = static_cast(b[gradientind[i]]); } B = PreNormalize(B, b0); if(m_NormalizationMethod == QBAR_SOLID_ANGLE) { vnl_vector coeffs(m_NumberCoefficients); coeffs = ( (*m_CoeffReconstructionMatrix) * B ); coeffs[0] += 1.0/(2.0*sqrt(QBALL_ANAL_RECON_PI)); odf = ( (*m_SphericalHarmonicBasisMatrix) * coeffs ).data_block(); } else if(m_NormalizationMethod == QBAR_NONNEG_SOLID_ANGLE) { /** this would be the place to implement a non-negative * solver for quadratic programming problem: * min .5*|| Bc-s ||^2 subject to -CLPc <= 4*pi*ones * (refer to MICCAI 2009 Goh et al. "Estimating ODFs with PDF constraints") * .5*|| Bc-s ||^2 == .5*c'B'Bc - x'B's + .5*s's */ QuadProgPP::Matrix& G(m_G); int lenb = B.size(); vnl_vector* s = new vnl_vector(lenb); for (int ii=0; ii g0_ = -1.0 * (*m_B_t) * (*s); QuadProgPP::Vector g0(g0_.data_block(),m_NumberCoefficients); try { QuadProgPP::QuadProg::solve_quadprog(G,g0,m_CE,m_ce0,m_CI,m_ci0,m_x); } catch(...) { m_x = 0; } vnl_vector coeffs(m_NumberCoefficients); for (int ii=0; ii void AnalyticalDiffusionQballReconstructionImageFilter ::tofile2(vnl_matrix *pA, std::string fname) { vnl_matrix A = (*pA); ofstream myfile; myfile.open (fname.c_str()); myfile << "A1=["; for(int i=0; i double AnalyticalDiffusionQballReconstructionImageFilter ::factorial(int number) { if(number <= 1) return 1; double result = 1.0; for(int i=1; i<=number; i++) result *= i; return result; } template< class T, class TG, class TO, int L, int NODF> void AnalyticalDiffusionQballReconstructionImageFilter ::Cart2Sph(double x, double y, double z, double *cart) { double phi, th, rad; rad = sqrt(x*x+y*y+z*z); th = atan2(z,sqrt(x*x+y*y)); phi = atan2(y,x); th = -th+QBALL_ANAL_RECON_PI/2; phi = -phi+QBALL_ANAL_RECON_PI; cart[0] = phi; cart[1] = th; cart[2] = rad; } template< class T, class TG, class TO, int L, int NODF> double AnalyticalDiffusionQballReconstructionImageFilter ::legendre0(int l) { if( l%2 != 0 ) { return 0; } else { double prod1 = 1.0; for(int i=1;i double AnalyticalDiffusionQballReconstructionImageFilter ::spherical_harmonic(int m,int l,double theta,double phi, bool complexPart) { if( theta < 0 || theta > QBALL_ANAL_RECON_PI ) { std::cout << "bad range" << std::endl; return 0; } if( phi < 0 || phi > 2*QBALL_ANAL_RECON_PI ) { std::cout << "bad range" << std::endl; return 0; } double pml = 0; double fac1 = factorial(l+m); double fac2 = factorial(l-m); if( m<0 ) { #if BOOST_VERSION / 100000 > 0 #if BOOST_VERSION / 100 % 1000 > 34 pml = ::boost::math::legendre_p(l, -m, cos(theta)); #else std::cout << "ERROR: Boost 1.35 minimum required" << std::endl; #endif #else std::cout << "ERROR: Boost 1.35 minimum required" << std::endl; #endif double mypow = pow(-1.0,-m); double myfac = (fac1/fac2); pml *= mypow*myfac; } else { #if BOOST_VERSION / 100000 > 0 #if BOOST_VERSION / 100 % 1000 > 34 pml = ::boost::math::legendre_p(l, m, cos(theta)); #endif #endif } //std::cout << "legendre(" << l << "," << m << "," << cos(theta) << ") = " << pml << std::endl; double retval = sqrt(((2.0*(double)l+1.0)/(4.0*QBALL_ANAL_RECON_PI))*(fac2/fac1)) * pml; if( !complexPart ) { retval *= cos(m*phi); } else { retval *= sin(m*phi); } //std::cout << retval << std::endl; return retval; } template< class T, class TG, class TO, int L, int NODF> double AnalyticalDiffusionQballReconstructionImageFilter ::Yj(int m, int k, double theta, double phi) { if( -k <= m && m < 0 ) { return sqrt(2.0) * spherical_harmonic(m,k,theta,phi,false); } if( m == 0 ) return spherical_harmonic(0,k,theta,phi,false); if( 0 < m && m <= k ) { return sqrt(2.0) * spherical_harmonic(m,k,theta,phi,true); } return 0; } template< class T, class TG, class TO, int L, int NODF> void AnalyticalDiffusionQballReconstructionImageFilter ::ComputeReconstructionMatrix() { //for(int i=-6;i<7;i++) // std::cout << boost::math::legendre_p(6, i, 0.65657) << std::endl; if( m_NumberOfGradientDirections < 6 ) { itkExceptionMacro( << "Not enough gradient directions supplied. Need to supply at least 6" ); } { // check for duplicate diffusion gradients bool warning = false; for(GradientDirectionContainerType::ConstIterator gdcit1 = this->m_GradientDirectionContainer->Begin(); gdcit1 != this->m_GradientDirectionContainer->End(); ++gdcit1) { for(GradientDirectionContainerType::ConstIterator gdcit2 = this->m_GradientDirectionContainer->Begin(); gdcit2 != this->m_GradientDirectionContainer->End(); ++gdcit2) { if(gdcit1.Value() == gdcit2.Value() && gdcit1.Index() != gdcit2.Index()) { itkWarningMacro( << "Some of the Diffusion Gradients equal each other. Corresponding image data should be averaged before calling this filter." ); warning = true; break; } } if (warning) break; } // handle acquisition schemes where only half of the spherical // shell is sampled by the gradient directions. In this case, // each gradient direction is duplicated in negative direction. vnl_vector centerMass(3); centerMass.fill(0.0); int count = 0; for(GradientDirectionContainerType::ConstIterator gdcit1 = this->m_GradientDirectionContainer->Begin(); gdcit1 != this->m_GradientDirectionContainer->End(); ++gdcit1) { if(gdcit1.Value().one_norm() > 0.0) { centerMass += gdcit1.Value(); count ++; } } centerMass /= count; if(centerMass.two_norm() > 0.1) { m_DirectionsDuplicated = true; m_NumberOfGradientDirections *= 2; } } vnl_matrix *Q = new vnl_matrix(3, m_NumberOfGradientDirections); { int i = 0; for(GradientDirectionContainerType::ConstIterator gdcit = this->m_GradientDirectionContainer->Begin(); gdcit != this->m_GradientDirectionContainer->End(); ++gdcit) { if(gdcit.Value().one_norm() > 0.0) { double x = gdcit.Value().get(0); double y = gdcit.Value().get(1); double z = gdcit.Value().get(2); double cart[3]; Cart2Sph(x,y,z,cart); (*Q)(0,i) = cart[0]; (*Q)(1,i) = cart[1]; (*Q)(2,i++) = cart[2]; } } if(m_DirectionsDuplicated) { for(GradientDirectionContainerType::ConstIterator gdcit = this->m_GradientDirectionContainer->Begin(); gdcit != this->m_GradientDirectionContainer->End(); ++gdcit) { if(gdcit.Value().one_norm() > 0.0) { double x = gdcit.Value().get(0); double y = gdcit.Value().get(1); double z = gdcit.Value().get(2); double cart[3]; Cart2Sph(x,y,z,cart); (*Q)(0,i) = cart[0]; (*Q)(1,i) = cart[1]; (*Q)(2,i++) = cart[2]; } } } } int l = L; m_NumberCoefficients = (int)(l*l + l + 2.0)/2.0 + l; vnl_matrix* B = new vnl_matrix(m_NumberOfGradientDirections,m_NumberCoefficients); vnl_matrix* _L = new vnl_matrix(m_NumberCoefficients,m_NumberCoefficients); _L->fill(0.0); vnl_matrix* LL = new vnl_matrix(m_NumberCoefficients,m_NumberCoefficients); LL->fill(0.0); vnl_matrix* P = new vnl_matrix(m_NumberCoefficients,m_NumberCoefficients); P->fill(0.0); vnl_matrix* Inv = new vnl_matrix(m_NumberCoefficients,m_NumberCoefficients); P->fill(0.0); vnl_vector* lj = new vnl_vector(m_NumberCoefficients); m_LP = new vnl_vector(m_NumberCoefficients); for(unsigned int i=0; i(B->transpose()); //tofile2(&m_B_t,"m_B_t"); vnl_matrix B_t_B = (*m_B_t) * (*B); //tofile2(&B_t_B,"B_t_B"); vnl_matrix lambdaLL(m_NumberCoefficients,m_NumberCoefficients); lambdaLL.update((*LL)); lambdaLL *= m_Lambda; //tofile2(&lambdaLL,"lLL"); vnl_matrix tmp( B_t_B + lambdaLL); vnl_matrix_inverse *pseudoInverse = new vnl_matrix_inverse( tmp ); (*Inv) = pseudoInverse->pinverse(); //tofile2(Inv,"Inv"); vnl_matrix temp((*Inv) * (*m_B_t)); double fac1 = (1.0/(16.0*QBALL_ANAL_RECON_PI*QBALL_ANAL_RECON_PI)); switch(m_NormalizationMethod) { case QBAR_ADC_ONLY: case QBAR_RAW_SIGNAL: break; case QBAR_STANDARD: case QBAR_B_ZERO_B_VALUE: case QBAR_B_ZERO: case QBAR_NONE: temp = (*P) * temp; break; case QBAR_SOLID_ANGLE: temp = fac1 * (*P) * (*_L) * temp; break; case QBAR_NONNEG_SOLID_ANGLE: m_G = QuadProgPP::Matrix(B_t_B.data_block(), B_t_B.rows(), B_t_B.cols()); m_CE = QuadProgPP::Matrix((double)0,m_NumberCoefficients,0); m_ce0 = QuadProgPP::Vector((double)0,0); m_ci0 = QuadProgPP::Vector(4*QBALL_ANAL_RECON_PI, NODF); m_x = QuadProgPP::Vector(m_NumberCoefficients); (*m_LP) *= fac1; break; } //tofile2(&temp,"A"); m_CoeffReconstructionMatrix = new vnl_matrix(m_NumberCoefficients,m_NumberOfGradientDirections); for(int i=0; iodfs later int NOdfDirections = NODF; vnl_matrix_fixed* U = itk::PointShell >::DistributePointShell(); m_SphericalHarmonicBasisMatrix = new vnl_matrix(NOdfDirections,m_NumberCoefficients); vnl_matrix* sphericalHarmonicBasisMatrix2 = new vnl_matrix(NOdfDirections,m_NumberCoefficients); for(int i=0; i CI_t = (*sphericalHarmonicBasisMatrix2) * (*P) * (*_L); m_CI = QuadProgPP::Matrix(CI_t.transpose().data_block(), m_NumberCoefficients, NOdfDirections); } m_ReconstructionMatrix = new vnl_matrix(NOdfDirections,m_NumberOfGradientDirections); *m_ReconstructionMatrix = (*m_SphericalHarmonicBasisMatrix) * (*m_CoeffReconstructionMatrix); } template< class T, class TG, class TO, int L, int NODF> void AnalyticalDiffusionQballReconstructionImageFilter ::SetGradientImage( GradientDirectionContainerType *gradientDirection, const GradientImagesType *gradientImage ) { this->m_GradientDirectionContainer = gradientDirection; unsigned int numImages = gradientDirection->Size(); this->m_NumberOfBaselineImages = 0; for(GradientDirectionContainerType::Iterator it = this->m_GradientDirectionContainer->Begin(); it != this->m_GradientDirectionContainer->End(); it++) { if(it.Value().one_norm() <= 0.0) { this->m_NumberOfBaselineImages++; } else // Normalize non-zero gradient directions { it.Value() = it.Value() / it.Value().two_norm(); } } this->m_NumberOfGradientDirections = numImages - this->m_NumberOfBaselineImages; // ensure that the gradient image we received has as many components as // the number of gradient directions if( gradientImage->GetVectorLength() != this->m_NumberOfBaselineImages + m_NumberOfGradientDirections ) { itkExceptionMacro( << m_NumberOfGradientDirections << " gradients + " << this->m_NumberOfBaselineImages << "baselines = " << m_NumberOfGradientDirections + this->m_NumberOfBaselineImages << " directions specified but image has " << gradientImage->GetVectorLength() << " components."); } this->ProcessObject::SetNthInput( 0, const_cast< GradientImagesType* >(gradientImage) ); } template< class T, class TG, class TO, int L, int NODF> void AnalyticalDiffusionQballReconstructionImageFilter ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os,indent); os << indent << "OdfReconstructionMatrix: " << m_ReconstructionMatrix << std::endl; if ( m_GradientDirectionContainer ) { os << indent << "GradientDirectionContainer: " << m_GradientDirectionContainer << std::endl; } else { os << indent << "GradientDirectionContainer: (Gradient directions not set)" << std::endl; } os << indent << "NumberOfGradientDirections: " << m_NumberOfGradientDirections << std::endl; os << indent << "NumberOfBaselineImages: " << m_NumberOfBaselineImages << std::endl; os << indent << "Threshold for reference B0 image: " << m_Threshold << std::endl; os << indent << "BValue: " << m_BValue << std::endl; } } #endif // __itkAnalyticalDiffusionQballReconstructionImageFilter_cpp diff --git a/Modules/DiffusionImaging/Reconstruction/itkAnalyticalDiffusionQballReconstructionImageFilter.h b/Modules/DiffusionImaging/Reconstruction/itkAnalyticalDiffusionQballReconstructionImageFilter.h index e76cf205e7..76391bc50f 100644 --- a/Modules/DiffusionImaging/Reconstruction/itkAnalyticalDiffusionQballReconstructionImageFilter.h +++ b/Modules/DiffusionImaging/Reconstruction/itkAnalyticalDiffusionQballReconstructionImageFilter.h @@ -1,300 +1,302 @@ /*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkDiffusionTensor3DReconstructionImageFilter.h,v $ Language: C++ Date: $Date: 2006-03-27 17:01:06 $ Version: $Revision: 1.12 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkAnalyticalDiffusionQballReconstructionImageFilter_h_ #define __itkAnalyticalDiffusionQballReconstructionImageFilter_h_ #include "itkImageToImageFilter.h" //#include "vnl/vnl_matrix.h" #include "vnl/vnl_vector_fixed.h" #include "vnl/vnl_matrix.h" #include "vnl/algo/vnl_svd.h" #include "itkVectorContainer.h" #include "itkVectorImage.h" #include "QuadProg.h" namespace itk{ /** \class AnalyticalDiffusionQballReconstructionImageFilter * \brief This class takes as input one or more reference image (acquired in the * absence of diffusion sensitizing gradients) and 'n' diffusion * weighted images and their gradient directions and computes an image of * orientation distribution function coefficients in a spherical harmonic basis. * * \par Inputs and Usage * \par * When you have the 'n' gradient and one or more reference images in a single * multi-component image (VectorImage), you can specify the images as * \code * filter->SetGradientImage( directionsContainer, vectorImage ); * \endcode * Note that this method is used to specify both the reference and gradient images. * This is convenient when the DWI images are read in using the * NRRD * format. Like the Nrrd format, the reference images are those components of the * vectorImage whose gradient direction is (0,0,0). If more than one reference image * is present, they are averaged prior to the reconstruction. * * \par Outputs * The output image is an image of vectors that must be understood as ODFs: * \code * Image< Vector< TPixelType, OdfNrDirections >, 3 > * \endcode * * \par Parameters * \li Threshold - Threshold on the reference image data. The output ODF will * be a null pdf for pixels in the reference image that have a value less * than this. * \li BValue - See the documentation of SetBValue(). * \li At least 6 gradient images must be specified for the filter to be able * to run. If the input gradient directions g_i are majorly sampled on one half * of the sqhere, then each input image I_i will be duplicated and assign -g_i * in order to guarantee stability of the algorithm. * \li OdfDirections - directions of resulting orientation distribution function * \li EquatorNrSamplingPoints - number of sampling points on equator when * performing Funk Radeon Transform (FRT) * \li BasisFunctionCenters - the centers of the basis functions are used for * the sRBF (spherical radial basis functions interpolation). If not set, they * will be defaulted to equal m_EquatorNrSamplingPoints * * \par Template parameters * The class is templated over * \li the pixel type of the reference and gradient images * (expected to be scalar data types) * \li the internal representation of the ODF pixels (double, float etc). * \li the number of OdfDirections * \li the number of basis function centers for the sRBF * * \par References: * \li[1] * Tuch DS, * "Q-ball imaging", Magn Reson Med. 2004 Dec;52(6):1358-72. * */ template< class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections> class AnalyticalDiffusionQballReconstructionImageFilter : public ImageToImageFilter< Image< TReferenceImagePixelType, 3 >, Image< Vector< TOdfPixelType, /*(NOrderL*NOrderL + NOrderL + 2.0)/2.0 + NOrderL*/NrOdfDirections >, 3 > > { public: enum Normalization { QBAR_STANDARD, QBAR_B_ZERO_B_VALUE, QBAR_B_ZERO, QBAR_NONE, QBAR_ADC_ONLY, QBAR_RAW_SIGNAL, QBAR_SOLID_ANGLE, QBAR_NONNEG_SOLID_ANGLE }; typedef AnalyticalDiffusionQballReconstructionImageFilter Self; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; typedef ImageToImageFilter< Image< TReferenceImagePixelType, 3>, Image< Vector< TOdfPixelType, /*NOrderL*NOrderL + NOrderL + 2.0)/2.0 + NOrderL*/NrOdfDirections >, 3 > > Superclass; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Runtime information support. */ itkTypeMacro(AnalyticalDiffusionQballReconstructionImageFilter, ImageToImageFilter); typedef TReferenceImagePixelType ReferencePixelType; typedef TGradientImagePixelType GradientPixelType; typedef Vector< TOdfPixelType, /*NOrderL*NOrderL + NOrderL + 2.0)/2.0 + NOrderL*/NrOdfDirections > OdfPixelType; /** Reference image data, This image is aquired in the absence * of a diffusion sensitizing field gradient */ typedef typename Superclass::InputImageType ReferenceImageType; typedef Image< OdfPixelType, 3 > OdfImageType; typedef OdfImageType OutputImageType; typedef TOdfPixelType BZeroPixelType; typedef Image< BZeroPixelType, 3 > BZeroImageType; typedef typename Superclass::OutputImageRegionType OutputImageRegionType; /** Typedef defining one (of the many) gradient images. */ typedef Image< GradientPixelType, 3 > GradientImageType; /** An alternative typedef defining one (of the many) gradient images. * It will be assumed that the vectorImage has the same dimension as the * Reference image and a vector length parameter of \c n (number of * gradient directions)*/ typedef VectorImage< GradientPixelType, 3 > GradientImagesType; /** Holds the ODF reconstruction matrix */ typedef vnl_matrix< TOdfPixelType >* OdfReconstructionMatrixType; typedef vnl_matrix< double > CoefficientMatrixType; /** Holds each magnetic field gradient used to acquire one DWImage */ typedef vnl_vector_fixed< double, 3 > GradientDirectionType; /** Container to hold gradient directions of the 'n' DW measurements */ typedef VectorContainer< unsigned int, GradientDirectionType > GradientDirectionContainerType; /** set method to add gradient directions and its corresponding * image. The image here is a VectorImage. The user is expected to pass the * gradient directions in a container. The ith element of the container * corresponds to the gradient direction of the ith component image the * VectorImage. For the baseline image, a vector of all zeros * should be set.*/ void SetGradientImage( GradientDirectionContainerType *, const GradientImagesType *image); /** Get reference image */ virtual ReferenceImageType * GetReferenceImage() { return ( static_cast< ReferenceImageType *>(this->ProcessObject::GetInput(0)) ); } /** Return the gradient direction. idx is 0 based */ virtual GradientDirectionType GetGradientDirection( unsigned int idx) const { if( idx >= m_NumberOfGradientDirections ) { itkExceptionMacro( << "Gradient direction " << idx << "does not exist" ); } return m_GradientDirectionContainer->ElementAt( idx+1 ); } static void tofile2(vnl_matrix *A, std::string fname); static double factorial(int number); static void Cart2Sph(double x, double y, double z, double* cart); static double legendre0(int l); static double spherical_harmonic(int m,int l,double theta,double phi, bool complexPart); static double Yj(int m, int k, double theta, double phi); OdfPixelType Normalize(OdfPixelType odf, typename NumericTraits::AccumulateType b0 ); vnl_vector PreNormalize( vnl_vector vec, typename NumericTraits::AccumulateType b0 ); /** Threshold on the reference image data. The output ODF will be a null * pdf for pixels in the reference image that have a value less than this * threshold. */ itkSetMacro( Threshold, ReferencePixelType ); itkGetMacro( Threshold, ReferencePixelType ); itkSetMacro( NormalizationMethod, Normalization); itkGetMacro( NormalizationMethod, Normalization ); + typedef Image BlaImage; itkGetMacro( BZeroImage, typename BZeroImageType::Pointer); + itkGetMacro( ODFSumImage, typename BlaImage::Pointer); itkSetMacro( BValue, TOdfPixelType); #ifdef GetBValue #undef GetBValue #endif itkGetConstReferenceMacro( BValue, TOdfPixelType); itkSetMacro( Lambda, double ); itkGetMacro( Lambda, double ); #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro(ReferenceEqualityComparableCheck, (Concept::EqualityComparable)); itkConceptMacro(TensorEqualityComparableCheck, (Concept::EqualityComparable)); itkConceptMacro(GradientConvertibleToDoubleCheck, (Concept::Convertible)); itkConceptMacro(DoubleConvertibleToTensorCheck, (Concept::Convertible)); itkConceptMacro(GradientReferenceAdditiveOperatorsCheck, (Concept::AdditiveOperators)); itkConceptMacro(ReferenceOStreamWritableCheck, (Concept::OStreamWritable)); itkConceptMacro(TensorOStreamWritableCheck, (Concept::OStreamWritable)); /** End concept checking */ #endif protected: AnalyticalDiffusionQballReconstructionImageFilter(); ~AnalyticalDiffusionQballReconstructionImageFilter() {}; void PrintSelf(std::ostream& os, Indent indent) const; void ComputeReconstructionMatrix(); void BeforeThreadedGenerateData(); void ThreadedGenerateData( const OutputImageRegionType &outputRegionForThread, int); private: OdfReconstructionMatrixType m_ReconstructionMatrix; OdfReconstructionMatrixType m_CoeffReconstructionMatrix; OdfReconstructionMatrixType m_SphericalHarmonicBasisMatrix; /** container to hold gradient directions */ GradientDirectionContainerType::Pointer m_GradientDirectionContainer; /** Number of gradient measurements */ unsigned int m_NumberOfGradientDirections; /** Number of baseline images */ unsigned int m_NumberOfBaselineImages; /** Threshold on the reference image data */ ReferencePixelType m_Threshold; /** LeBihan's b-value for normalizing tensors */ TOdfPixelType m_BValue; typename BZeroImageType::Pointer m_BZeroImage; double m_Lambda; bool m_DirectionsDuplicated; Normalization m_NormalizationMethod; int m_NumberCoefficients; QuadProgPP::Matrix m_G, m_CE, m_CI; QuadProgPP::Vector m_g0, m_ce0, m_ci0, m_x; vnl_matrix* m_B_t; vnl_vector* m_LP; - + BlaImage::Pointer m_ODFSumImage; }; } #ifndef ITK_MANUAL_INSTANTIATION #include "itkAnalyticalDiffusionQballReconstructionImageFilter.cpp" #endif #endif //__itkAnalyticalDiffusionQballReconstructionImageFilter_h_ diff --git a/Modules/DiffusionImaging/Reconstruction/itkOrientationDistributionFunction.txx b/Modules/DiffusionImaging/Reconstruction/itkOrientationDistributionFunction.txx index e23c4cbecf..c084aee2d5 100644 --- a/Modules/DiffusionImaging/Reconstruction/itkOrientationDistributionFunction.txx +++ b/Modules/DiffusionImaging/Reconstruction/itkOrientationDistributionFunction.txx @@ -1,1234 +1,1234 @@ /*========================================================================= Program: Insight Segmentation & Registration Toolkit Language: C++ Date: $Date: 2008-03-10 22:48:13 $ Version: $Revision: 1.14 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkOrientationDistributionFunction_txx #define _itkOrientationDistributionFunction_txx #include #include #include #include #include #include #include "itkPointShell.h" //#include "itkNumericTraitsTensorPixel.h" namespace itk { /* #define INIT_STATIC_ODF_VARS(N_DIRS) \ _INIT_STATIC_ODF_VARS(float,N_DIRS) \ _INIT_STATIC_ODF_VARS(double,N_DIRS) \ #define _INIT_STATIC_ODF_VARS(PIXTYPE,N_DIRS) \ vtkPolyData* itk::OrientationDistributionFunction::m_BaseMesh = NULL; \ vnl_matrix_fixed* itk::OrientationDistributionFunction::m_Directions \ = itk::PointShell >::DistributePointShell(); \ std::vector< std::vector* >* itk::OrientationDistributionFunction::m_NeighborIdxs = NULL; \ std::vector* itk::OrientationDistributionFunction::m_HalfSphereIdxs = NULL; \ bool itk::OrientationDistributionFunction::m_Mutex = false; \ INIT_STATIC_ODF_VARS(40) INIT_STATIC_ODF_VARS(60) INIT_STATIC_ODF_VARS(80) INIT_STATIC_ODF_VARS(100) INIT_STATIC_ODF_VARS(150) INIT_STATIC_ODF_VARS(200) INIT_STATIC_ODF_VARS(250) */ template vtkPolyData* itk::OrientationDistributionFunction::m_BaseMesh = NULL; template double itk::OrientationDistributionFunction::m_MaxChordLength = -1.0; template vnl_matrix_fixed* itk::OrientationDistributionFunction::m_Directions = itk::PointShell >::DistributePointShell(); template std::vector< std::vector* >* itk::OrientationDistributionFunction::m_NeighborIdxs = NULL; template std::vector< std::vector* >* itk::OrientationDistributionFunction::m_AngularRangeIdxs = NULL; template std::vector* itk::OrientationDistributionFunction::m_HalfSphereIdxs = NULL; template itk::SimpleFastMutexLock itk::OrientationDistributionFunction::m_MutexBaseMesh; template itk::SimpleFastMutexLock itk::OrientationDistributionFunction::m_MutexHalfSphereIdxs; template itk::SimpleFastMutexLock itk::OrientationDistributionFunction::m_MutexNeighbors; template itk::SimpleFastMutexLock itk::OrientationDistributionFunction::m_MutexAngularRange; #define ODF_PI 3.14159265358979323846 /* * Assignment Operator */ template OrientationDistributionFunction& OrientationDistributionFunction ::operator= (const Self& r) { BaseArray::operator=(r); return *this; } /* * Assignment Operator from a scalar constant */ template OrientationDistributionFunction& OrientationDistributionFunction ::operator= (const ComponentType & r) { BaseArray::operator=(&r); return *this; } /* * Assigment from a plain array */ template OrientationDistributionFunction& OrientationDistributionFunction ::operator= (const ComponentArrayType r ) { BaseArray::operator=(r); return *this; } /** * Returns a temporary copy of a vector */ template OrientationDistributionFunction OrientationDistributionFunction ::operator+(const Self & r) const { Self result; for( unsigned int i=0; i OrientationDistributionFunction OrientationDistributionFunction ::operator-(const Self & r) const { Self result; for( unsigned int i=0; i const OrientationDistributionFunction & OrientationDistributionFunction ::operator+=(const Self & r) { for( unsigned int i=0; i const OrientationDistributionFunction & OrientationDistributionFunction ::operator-=(const Self & r) { for( unsigned int i=0; i const OrientationDistributionFunction & OrientationDistributionFunction ::operator*=(const RealValueType & r) { for( unsigned int i=0; i const OrientationDistributionFunction & OrientationDistributionFunction ::operator/=(const RealValueType & r) { for( unsigned int i=0; i OrientationDistributionFunction OrientationDistributionFunction ::operator*(const RealValueType & r) const { Self result; for( unsigned int i=0; i OrientationDistributionFunction OrientationDistributionFunction ::operator/(const RealValueType & r) const { Self result; for( unsigned int i=0; i const typename OrientationDistributionFunction::ValueType & OrientationDistributionFunction ::operator()(unsigned int row, unsigned int col) const { unsigned int k; if( row < col ) { k = row * InternalDimension + col - row * ( row + 1 ) / 2; } else { k = col * InternalDimension + row - col * ( col + 1 ) / 2; } if( k >= InternalDimension ) { k = 0; } return (*this)[k]; } /* * Matrix notation access to elements */ template typename OrientationDistributionFunction::ValueType & OrientationDistributionFunction ::operator()(unsigned int row, unsigned int col) { unsigned int k; if( row < col ) { k = row * InternalDimension + col - row * ( row + 1 ) / 2; } else { k = col * InternalDimension + row - col * ( col + 1 ) / 2; } if( k >= InternalDimension ) { k = 0; } return (*this)[k]; } /* * Set the Tensor to an Identity. * Set ones in the diagonal and zeroes every where else. */ template void OrientationDistributionFunction ::SetIsotropic() { this->Fill(NumericTraits< T >::One / NOdfDirections); } /* * Set the Tensor to an Identity. * Set ones in the diagonal and zeroes every where else. */ template void OrientationDistributionFunction ::InitFromTensor(itk::DiffusionTensor3D tensor) { for(unsigned int i=0; i void OrientationDistributionFunction ::L2Normalize() { T sum = 0; for( unsigned int i=0; i void OrientationDistributionFunction ::Normalize() { T sum = 0; for( unsigned int i=0; i OrientationDistributionFunction OrientationDistributionFunction ::MinMaxNormalize() const { T max = NumericTraits::NonpositiveMin(); T min = NumericTraits::max(); for( unsigned int i=0; i max ? (*this)[i] : max; min = (*this)[i] < min ? (*this)[i] : min; } Self retval; for( unsigned int i=0; i OrientationDistributionFunction OrientationDistributionFunction ::MaxNormalize() const { T max = NumericTraits::NonpositiveMin(); for( unsigned int i=0; i max ? (*this)[i] : max; } Self retval; for( unsigned int i=0; i double OrientationDistributionFunction ::GetMaxChordLength() { if(m_MaxChordLength<0.0) { ComputeBaseMesh(); double max_dist = -1; vtkPoints* points = m_BaseMesh->GetPoints(); for(int i=0; iGetPoint(i,p); std::vector neighbors = GetNeighbors(i); for(int j=0; jGetPoint(neighbors[j],n); double d = sqrt( (p[0]-n[0])*(p[0]-n[0]) + (p[1]-n[1])*(p[1]-n[1]) + (p[2]-n[2])*(p[2]-n[2])); max_dist = d>max_dist ? d : max_dist; } } m_MaxChordLength = max_dist; } return m_MaxChordLength; } template void OrientationDistributionFunction ::ComputeBaseMesh() { m_MutexBaseMesh.Lock(); if(m_BaseMesh == NULL) { vtkPoints* points = vtkPoints::New(); for(unsigned int j=0; jInsertNextPoint(az,elev,r); } vtkPolyData* polydata = vtkPolyData::New(); polydata->SetPoints( points ); vtkDelaunay2D *delaunay = vtkDelaunay2D::New(); delaunay->SetInput( polydata ); delaunay->Update(); vtkCellArray* vtkpolys = delaunay->GetOutput()->GetPolys(); vtkCellArray* vtknewpolys = vtkCellArray::New(); vtkIdType npts; vtkIdType *pts; while(vtkpolys->GetNextCell(npts,pts)) { bool insert = true; for(int i=0; iGetPoint(pts[i]); double az = tmpPoint[0]; double elev = tmpPoint[1]; if((abs(az)>ODF_PI-0.5) || (abs(elev)>ODF_PI/2-0.5)) insert = false; } if(insert) vtknewpolys->InsertNextCell(npts, pts); } vtkPoints* points2 = vtkPoints::New(); for(unsigned int j=0; jInsertNextPoint(az,elev,r); } vtkPolyData* polydata2 = vtkPolyData::New(); polydata2->SetPoints( points2 ); vtkDelaunay2D *delaunay2 = vtkDelaunay2D::New(); delaunay2->SetInput( polydata2 ); delaunay2->Update(); vtkpolys = delaunay2->GetOutput()->GetPolys(); while(vtkpolys->GetNextCell(npts,pts)) { bool insert = true; for(int i=0; iGetPoint(pts[i]); double az = tmpPoint[0]; double elev = tmpPoint[1]; if((abs(az)>ODF_PI-0.5) || (abs(elev)>ODF_PI/2-0.5)) insert = false; } if(insert) vtknewpolys->InsertNextCell(npts, pts); } polydata->SetPolys(vtknewpolys); for (vtkIdType p = 0; p < NOdfDirections; p++) { points->SetPoint(p,m_Directions->get_column(p).data_block()); } polydata->SetPoints( points ); ////clean up the poly data to remove redundant points //vtkCleanPolyData* cleaner = vtkCleanPolyData::New(); //cleaner->SetInput( polydata ); //cleaner->SetAbsoluteTolerance( 0.0 ); //cleaner->Update(); //vtkAppendPolyData* append = vtkAppendPolyData::New(); //append->AddInput(cleaner->GetOutput()); //append->Update(); //m_BaseMesh = append->GetOutput(); m_BaseMesh = polydata; } m_MutexBaseMesh.Unlock(); } /* * Extract the principle diffusion direction */ template int OrientationDistributionFunction ::GetPrincipleDiffusionDirection() const { T max = NumericTraits::NonpositiveMin(); int maxidx = -1; for( unsigned int i=0; i max ) + if((*this)[i] >= max ) { max = (*this)[i]; maxidx = i; } } return maxidx; } template std::vector OrientationDistributionFunction ::GetNeighbors(int idx) { ComputeBaseMesh(); m_MutexNeighbors.Lock(); if(m_NeighborIdxs == NULL) { m_NeighborIdxs = new std::vector< std::vector* >(); vtkCellArray* polys = m_BaseMesh->GetPolys(); for(unsigned int i=0; i *idxs = new std::vector(); polys->InitTraversal(); vtkIdType npts; vtkIdType *pts; while(polys->GetNextCell(npts,pts)) { if( pts[0] == i ) { idxs->push_back(pts[1]); idxs->push_back(pts[2]); } else if( pts[1] == i ) { idxs->push_back(pts[0]); idxs->push_back(pts[2]); } else if( pts[2] == i ) { idxs->push_back(pts[0]); idxs->push_back(pts[1]); } } std::sort(idxs->begin(), idxs->end()); std::vector< int >::iterator endLocation; endLocation = std::unique( idxs->begin(), idxs->end() ); idxs->erase(endLocation, idxs->end()); m_NeighborIdxs->push_back(idxs); } } m_MutexNeighbors.Unlock(); return *m_NeighborIdxs->at(idx); } /* * Extract the n-th diffusion direction */ template int OrientationDistributionFunction ::GetNthDiffusionDirection(int n, vnl_vector_fixed rndVec) const { if( n == 0 ) return GetPrincipleDiffusionDirection(); m_MutexHalfSphereIdxs.Lock(); if( !m_HalfSphereIdxs ) { m_HalfSphereIdxs = new std::vector(); for( unsigned int i=0; iget_column(i),rndVec) > 0.0) { m_HalfSphereIdxs->push_back(i); } } } m_MutexHalfSphereIdxs.Unlock(); // collect indices of directions // that are local maxima std::vector localMaxima; std::vector::iterator it; for( it=m_HalfSphereIdxs->begin(); it!=m_HalfSphereIdxs->end(); it++) { std::vector nbs = GetNeighbors(*it); std::vector::iterator it2; bool max = true; for(it2 = nbs.begin(); it2 != nbs.end(); it2++) { if((*this)[*it2] > (*this)[*it]) { max = false; break; } } if(max) localMaxima.push_back(*it); } // delete n highest local maxima from list // and return remaining highest int maxidx = -1; std::vector::iterator itMax; for( int i=0; i<=n; i++ ) { maxidx = -1; T max = NumericTraits::NonpositiveMin(); for(it = localMaxima.begin(); it != localMaxima.end(); it++) { if((*this)[*it]>max) { max = (*this)[*it]; maxidx = *it; } } it = find(localMaxima.begin(), localMaxima.end(), maxidx); if(it!=localMaxima.end()) localMaxima.erase(it); } return maxidx; } template < typename TComponent, unsigned int NOdfDirections > vnl_vector_fixed itk::OrientationDistributionFunction ::GetDirection( int i ) { return m_Directions->get_column(i); } /* * Interpolate a position between sampled directions */ template T OrientationDistributionFunction ::GetInterpolatedComponent(vnl_vector_fixed dir, InterpolationMethods method) const { ComputeBaseMesh(); double retval = -1.0; switch(method) { case ODF_NEAREST_NEIGHBOR_INTERP: { vtkPoints* points = m_BaseMesh->GetPoints(); double current_min = NumericTraits::max(); int current_min_idx = -1; for(int i=0; i P(points->GetPoint(i)); double dist = (dir-P).two_norm(); current_min_idx = distGetNthComponent(current_min_idx); break; } case ODF_TRILINEAR_BARYCENTRIC_INTERP: { double maxChordLength = GetMaxChordLength(); vtkCellArray* polys = m_BaseMesh->GetPolys(); vtkPoints* points = m_BaseMesh->GetPoints(); vtkIdType npts; vtkIdType *pts; double current_min = NumericTraits::max(); polys->InitTraversal(); while(polys->GetNextCell(npts,pts)) { vnl_vector_fixed A(points->GetPoint(pts[0])); vnl_vector_fixed B(points->GetPoint(pts[1])); vnl_vector_fixed C(points->GetPoint(pts[2])); vnl_vector_fixed d1; d1.put(0,(dir-A).two_norm()); d1.put(1,(dir-B).two_norm()); d1.put(2,(dir-C).two_norm()); double maxval = d1.max_value(); if(maxval>maxChordLength) { continue; } // Compute vectors vnl_vector_fixed v0 = C - A; vnl_vector_fixed v1 = B - A; // Project direction to plane ABC vnl_vector_fixed v6 = dir; vnl_vector_fixed cross = vnl_cross_3d(v0, v1); cross = cross.normalize(); vtkPlane::ProjectPoint(v6.data_block(),A.data_block(),cross.data_block(),v6.data_block()); v6 = v6-A; // Calculate barycentric coords vnl_matrix_fixed mat; mat.set_column(0, v0); mat.set_column(1, v1); vnl_matrix_inverse inv(mat); vnl_matrix_fixed inver = inv.pinverse(); vnl_vector uv = inv.pinverse()*v6; // Check if point is in triangle double eps = 0.01; if( (uv(0) >= 0-eps) && (uv(1) >= 0-eps) && (uv(0) + uv(1) <= 1+eps) ) { // check if minimum angle is the max so far if(d1.two_norm() < current_min) { current_min = d1.two_norm(); vnl_vector barycentricCoords(3); barycentricCoords[2] = uv[0]<0 ? 0 : (uv[0]>1?1:uv[0]); barycentricCoords[1] = uv[1]<0 ? 0 : (uv[1]>1?1:uv[1]); barycentricCoords[0] = 1-(barycentricCoords[1]+barycentricCoords[2]); retval = barycentricCoords[0]*this->GetNthComponent(pts[0]) + barycentricCoords[1]*this->GetNthComponent(pts[1]) + barycentricCoords[2]*this->GetNthComponent(pts[2]); } } } break; } case ODF_SPHERICAL_GAUSSIAN_BASIS_FUNCTIONS: { double maxChordLength = GetMaxChordLength(); double sigma = asin(maxChordLength/2); // this is the contribution of each kernel to each sampling point on the // equator vnl_vector contrib; contrib.set_size(NOdfDirections); vtkPoints* points = m_BaseMesh->GetPoints(); double sum = 0; for(int i=0; i P(points->GetPoint(i)); double stv = dir[0]*P[0] + dir[1]*P[1] + dir[2]*P[2]; stv = (stv<-1.0) ? -1.0 : ( (stv>1.0) ? 1.0 : stv); double x = acos(stv); contrib[i] = (1.0/(sigma*sqrt(2.0*ODF_PI))) *exp((-x*x)/(2*sigma*sigma)); sum += contrib[i]; } retval = 0; for(int i=0; iGetNthComponent(i); } break; } } if(retval==-1) { std::cout << "Interpolation failed" << std::endl; return 0; } return retval; } /* * Calculate Generalized Fractional Anisotropy */ template T OrientationDistributionFunction ::GetGeneralizedFractionalAnisotropy() const { double mean = 0; double std = 0; double rms = 0; for( unsigned int i=0; i T itk::OrientationDistributionFunction ::GetGeneralizedGFA( int k, int p ) const { double mean = 0; double std = 0; double rms = 0; double max = NumericTraits::NonpositiveMin(); for( unsigned int i=0; i max ? val : max; } max = pow(max,(double)p); mean /= N; for( unsigned int i=0; i0) { rms += pow(val,(double)(p*k)); } } std /= N - 1; std = sqrt(std); if(k>0) { rms /= N; rms = pow(rms,(double)(1.0/k)); } else if(k<0) // lim k->inf gives us the maximum { rms = max; } else // k==0 undefined, we define zeros root from 1 as 1 { rms = 1; } if(rms == 0) { return 0; } else { return (T)(std/rms); } } /* * Calculate Nematic Order Parameter */ template < typename T, unsigned int N > T itk::OrientationDistributionFunction ::GetNematicOrderParameter() const { // not yet implemented return 0; } /* * Calculate StdDev by MaxValue */ template < typename T, unsigned int N > T itk::OrientationDistributionFunction ::GetStdDevByMaxValue() const { double mean = 0; double std = 0; T max = NumericTraits::NonpositiveMin(); for( unsigned int i=0; i max ? (*this)[i] : max; } mean /= InternalDimension; for( unsigned int i=0; i T itk::OrientationDistributionFunction ::GetPrincipleCurvature(double alphaMinDegree, double alphaMaxDegree, int invert) const { // following loop only performed once // (computing indices of each angular range) m_MutexAngularRange.Lock(); if(m_AngularRangeIdxs == NULL) { m_AngularRangeIdxs = new std::vector< std::vector* >(); for(unsigned int i=0; i pDir = GetDirection(i); std::vector *idxs = new std::vector(); for(unsigned int j=0; j cDir = GetDirection(j); double angle = ( 180 / ODF_PI ) * acos( dot_product(pDir, cDir) ); if( (angle < alphaMaxDegree) && (angle > alphaMinDegree) ) { idxs->push_back(j); } } m_AngularRangeIdxs->push_back(idxs); } } m_MutexAngularRange.Unlock(); // find the maximum (or minimum) direction (remember index and value) T mode; int pIdx = -1; if(invert == 0) { pIdx = GetPrincipleDiffusionDirection(); mode = (*this)[pIdx]; } else { mode = NumericTraits::max(); for( unsigned int i=0; i nbs = GetNeighbors(pIdx); //////std::vector modeAndNeighborVals; //////modeAndNeighborVals.push_back(mode); //////int numNeighbors = nbs.size(); //////for(int i=0; i odfValuesInAngularRange; int numInRange = m_AngularRangeIdxs->at(pIdx)->size(); for(int i=0; iat(pIdx))[i] ]); } // sort them by value std::sort( odfValuesInAngularRange.begin(), odfValuesInAngularRange.end() ); // median of angular range T median = odfValuesInAngularRange[floor(quantile*(double)numInRange+0.5)]; // compute and return final value if(mode > median) { return mode/median - 1.0; } else { return median/mode - 1.0; } } /* * Calculate Normalized Entropy */ template < typename T, unsigned int N > T itk::OrientationDistributionFunction ::GetNormalizedEntropy() const { double mean = 0; for( unsigned int i=0; i OrientationDistributionFunction OrientationDistributionFunction ::PreMultiply( const MatrixType & m ) const { Self result; typedef typename NumericTraits::AccumulateType AccumulateType; for(unsigned int r=0; r::ZeroValue(); for(unsigned int t=0; t( sum ); } } return result; } /* * Post-multiply the Tensor by a Matrix */ template OrientationDistributionFunction OrientationDistributionFunction ::PostMultiply( const MatrixType & m ) const { Self result; typedef typename NumericTraits::AccumulateType AccumulateType; for(unsigned int r=0; r::ZeroValue(); for(unsigned int t=0; t( sum ); } } return result; } /** * Print content to an ostream */ template std::ostream & operator<<(std::ostream& os,const OrientationDistributionFunction & c ) { for(unsigned int i=0; i::PrintType>(c[i]) << " "; } return os; } /** * Read content from an istream */ template std::istream & operator>>(std::istream& is, OrientationDistributionFunction & dt ) { for(unsigned int i=0; i < dt.GetNumberOfComponents(); i++) { is >> dt[i]; } return is; } } // end namespace itk #endif