diff --git a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp index d5edddae65..a6d9eb6219 100644 --- a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp +++ b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp @@ -1,988 +1,986 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include #include "itkStreamlineTrackingFilter.h" #include #include #include #include #include "itkPointShell.h" #include #include #include #include #include #include #include #include #define _USE_MATH_DEFINES #include namespace itk { StreamlineTrackingFilter ::StreamlineTrackingFilter() : m_PauseTracking(false) , m_AbortTracking(false) , m_BuildFibersFinished(false) , m_BuildFibersReady(0) , m_FiberPolyData(nullptr) , m_Points(nullptr) , m_Cells(nullptr) , m_StoppingRegions(nullptr) , m_TargetRegions(nullptr) , m_SeedImage(nullptr) , m_MaskImage(nullptr) + , m_ExclusionRegions(nullptr) , m_OutputProbabilityMap(nullptr) , m_MinVoxelSize(-1) , m_AngularThresholdDeg(-1) , m_StepSizeVox(-1) , m_SamplingDistanceVox(-1) , m_AngularThreshold(-1) , m_StepSize(0) , m_MaxLength(10000) , m_MinTractLength(20.0) , m_MaxTractLength(400.0) , m_SeedsPerVoxel(1) , m_AvoidStop(true) , m_RandomSampling(false) , m_SamplingDistance(-1) , m_DeflectionMod(1.0) , m_OnlyForwardSamples(true) , m_UseStopVotes(true) , m_NumberOfSamples(30) , m_NumPreviousDirections(1) , m_MaxNumTracts(-1) , m_Verbose(true) , m_LoopCheck(-1) , m_DemoMode(false) , m_Random(true) , m_UseOutputProbabilityMap(false) , m_CurrentTracts(0) , m_Progress(0) , m_StopTracking(false) , m_InterpolateMasks(true) , m_TrialsPerSeed(10) , m_EndpointConstraint(EndpointConstraints::NONE) { this->SetNumberOfRequiredInputs(0); } std::string StreamlineTrackingFilter::GetStatusText() { std::string status = "Seedpoints processed: " + boost::lexical_cast(m_Progress) + "/" + boost::lexical_cast(m_SeedPoints.size()); if (m_SeedPoints.size()>0) status += " (" + boost::lexical_cast(100*m_Progress/m_SeedPoints.size()) + "%)"; if (m_MaxNumTracts>0) status += "\nFibers accepted: " + boost::lexical_cast(m_CurrentTracts) + "/" + boost::lexical_cast(m_MaxNumTracts); else status += "\nFibers accepted: " + boost::lexical_cast(m_CurrentTracts); return status; } void StreamlineTrackingFilter::BeforeTracking() { m_StopTracking = false; m_TrackingHandler->SetRandom(m_Random); m_TrackingHandler->InitForTracking(); m_FiberPolyData = PolyDataType::New(); m_Points = vtkSmartPointer< vtkPoints >::New(); m_Cells = vtkSmartPointer< vtkCellArray >::New(); itk::Vector< double, 3 > imageSpacing = m_TrackingHandler->GetSpacing(); if(imageSpacing[0]SetAngularThreshold(m_AngularThreshold); if (m_SamplingDistanceVoxGetNumberOfThreads(); i++) { PolyDataType poly = PolyDataType::New(); m_PolyDataContainer.push_back(poly); } if (m_UseOutputProbabilityMap) { m_OutputProbabilityMap = ItkDoubleImgType::New(); m_OutputProbabilityMap->SetSpacing(imageSpacing); m_OutputProbabilityMap->SetOrigin(m_TrackingHandler->GetOrigin()); m_OutputProbabilityMap->SetDirection(m_TrackingHandler->GetDirection()); m_OutputProbabilityMap->SetRegions(m_TrackingHandler->GetLargestPossibleRegion()); m_OutputProbabilityMap->Allocate(); m_OutputProbabilityMap->FillBuffer(0); } m_MaskInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); m_StopInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); m_SeedInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); m_TargetInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); + m_ExclusionInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); if (m_StoppingRegions.IsNull()) { m_StoppingRegions = ItkFloatImgType::New(); m_StoppingRegions->SetSpacing( imageSpacing ); m_StoppingRegions->SetOrigin( m_TrackingHandler->GetOrigin() ); m_StoppingRegions->SetDirection( m_TrackingHandler->GetDirection() ); m_StoppingRegions->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_StoppingRegions->Allocate(); m_StoppingRegions->FillBuffer(0); } else std::cout << "StreamlineTracking - Using stopping region image" << std::endl; m_StopInterpolator->SetInputImage(m_StoppingRegions); + if (m_ExclusionRegions.IsNotNull()) + { + std::cout << "StreamlineTracking - Using exclusion region image" << std::endl; + m_ExclusionInterpolator->SetInputImage(m_ExclusionRegions); + } + if (m_TargetRegions.IsNull()) { m_TargetImageSet = false; m_TargetRegions = ItkFloatImgType::New(); m_TargetRegions->SetSpacing( imageSpacing ); m_TargetRegions->SetOrigin( m_TrackingHandler->GetOrigin() ); m_TargetRegions->SetDirection( m_TrackingHandler->GetDirection() ); m_TargetRegions->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_TargetRegions->Allocate(); m_TargetRegions->FillBuffer(1); } else { m_TargetImageSet = true; m_TargetInterpolator->SetInputImage(m_TargetRegions); std::cout << "StreamlineTracking - Using target region image" << std::endl; } if (m_SeedImage.IsNull()) { m_SeedImageSet = false; m_SeedImage = ItkFloatImgType::New(); m_SeedImage->SetSpacing( imageSpacing ); m_SeedImage->SetOrigin( m_TrackingHandler->GetOrigin() ); m_SeedImage->SetDirection( m_TrackingHandler->GetDirection() ); m_SeedImage->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_SeedImage->Allocate(); m_SeedImage->FillBuffer(1); } else { m_SeedImageSet = true; std::cout << "StreamlineTracking - Using seed image" << std::endl; } m_SeedInterpolator->SetInputImage(m_SeedImage); if (m_MaskImage.IsNull()) { // initialize mask image m_MaskImage = ItkFloatImgType::New(); m_MaskImage->SetSpacing( imageSpacing ); m_MaskImage->SetOrigin( m_TrackingHandler->GetOrigin() ); m_MaskImage->SetDirection( m_TrackingHandler->GetDirection() ); m_MaskImage->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_MaskImage->Allocate(); m_MaskImage->FillBuffer(1); } else std::cout << "StreamlineTracking - Using mask image" << std::endl; m_MaskInterpolator->SetInputImage(m_MaskImage); // Autosettings for endpoint constraints if (m_EndpointConstraint==EndpointConstraints::NONE && m_TargetImageSet && m_SeedImageSet) { MITK_INFO << "No endpoint constraint chosen but seed and target image set --> setting constraint to EPS_IN_SEED_AND_TARGET"; m_EndpointConstraint = EndpointConstraints::EPS_IN_SEED_AND_TARGET; } else if (m_EndpointConstraint==EndpointConstraints::NONE && m_TargetImageSet) { MITK_INFO << "No endpoint constraint chosen but target image set --> setting constraint to EPS_IN_TARGET"; m_EndpointConstraint = EndpointConstraints::EPS_IN_TARGET; } // Check if endpoint constraints are valid FiberType test_fib; itk::Point p; p.Fill(0); test_fib.push_back(p); test_fib.push_back(p); IsValidFiber(&test_fib); if (m_SeedPoints.empty()) GetSeedPointsFromSeedImage(); m_BuildFibersReady = 0; m_BuildFibersFinished = false; m_Tractogram.clear(); m_SamplingPointset = mitk::PointSet::New(); m_AlternativePointset = mitk::PointSet::New(); m_StopVotePointset = mitk::PointSet::New(); m_StartTime = std::chrono::system_clock::now(); if (m_DemoMode) omp_set_num_threads(1); if (m_TrackingHandler->GetMode()==mitk::TrackingDataHandler::MODE::DETERMINISTIC) std::cout << "StreamlineTracking - Mode: deterministic" << std::endl; else if(m_TrackingHandler->GetMode()==mitk::TrackingDataHandler::MODE::PROBABILISTIC) { std::cout << "StreamlineTracking - Mode: probabilistic" << std::endl; std::cout << "StreamlineTracking - Trials per seed: " << m_TrialsPerSeed << std::endl; } else std::cout << "StreamlineTracking - Mode: ???" << std::endl; if (m_EndpointConstraint==EndpointConstraints::NONE) std::cout << "StreamlineTracking - Endpoint constraint: NONE" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: EPS_IN_TARGET" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_TARGET_LABELDIFF) std::cout << "StreamlineTracking - Endpoint constraint: EPS_IN_TARGET_LABELDIFF" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_SEED_AND_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: EPS_IN_SEED_AND_TARGET" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::MIN_ONE_EP_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: MIN_ONE_EP_IN_TARGET" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::ONE_EP_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: ONE_EP_IN_TARGET" << std::endl; else if (m_EndpointConstraint==EndpointConstraints::NO_EP_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: NO_EP_IN_TARGET" << std::endl; std::cout << "StreamlineTracking - Angular threshold: " << m_AngularThreshold << " (" << 180*std::acos( m_AngularThreshold )/M_PI << "°)" << std::endl; std::cout << "StreamlineTracking - Stepsize: " << m_StepSize << "mm (" << m_StepSize/m_MinVoxelSize << "*vox)" << std::endl; std::cout << "StreamlineTracking - Seeds per voxel: " << m_SeedsPerVoxel << std::endl; std::cout << "StreamlineTracking - Max. tract length: " << m_MaxTractLength << "mm" << std::endl; std::cout << "StreamlineTracking - Min. tract length: " << m_MinTractLength << "mm" << std::endl; std::cout << "StreamlineTracking - Max. num. tracts: " << m_MaxNumTracts << std::endl; std::cout << "StreamlineTracking - Loop check: " << m_LoopCheck << "°" << std::endl; std::cout << "StreamlineTracking - Num. neighborhood samples: " << m_NumberOfSamples << std::endl; std::cout << "StreamlineTracking - Max. sampling distance: " << m_SamplingDistance << "mm (" << m_SamplingDistance/m_MinVoxelSize << "*vox)" << std::endl; std::cout << "StreamlineTracking - Deflection modifier: " << m_DeflectionMod << std::endl; std::cout << "StreamlineTracking - Use stop votes: " << m_UseStopVotes << std::endl; std::cout << "StreamlineTracking - Only frontal samples: " << m_OnlyForwardSamples << std::endl; if (m_DemoMode) { std::cout << "StreamlineTracking - Running in demo mode"; std::cout << "StreamlineTracking - Starting streamline tracking using 1 thread" << std::endl; } else std::cout << "StreamlineTracking - Starting streamline tracking using " << omp_get_max_threads() << " threads" << std::endl; } void StreamlineTrackingFilter::CalculateNewPosition(itk::Point& pos, vnl_vector_fixed& dir) { pos[0] += dir[0]*m_StepSize; pos[1] += dir[1]*m_StepSize; pos[2] += dir[2]*m_StepSize; } std::vector< vnl_vector_fixed > StreamlineTrackingFilter::CreateDirections(int NPoints) { std::vector< vnl_vector_fixed > pointshell; if (NPoints<2) return pointshell; std::vector< float > theta; theta.resize(NPoints); std::vector< float > phi; phi.resize(NPoints); float C = sqrt(4*M_PI); phi[0] = 0.0; phi[NPoints-1] = 0.0; for(int i=0; i0 && i d; d[0] = cos(theta[i]) * cos(phi[i]); d[1] = cos(theta[i]) * sin(phi[i]); d[2] = sin(theta[i]); pointshell.push_back(d); } return pointshell; } vnl_vector_fixed StreamlineTrackingFilter::GetNewDirection(itk::Point &pos, std::deque >& olddirs, itk::Index<3> &oldIndex) { if (m_DemoMode) { m_SamplingPointset->Clear(); m_AlternativePointset->Clear(); m_StopVotePointset->Clear(); } vnl_vector_fixed direction; direction.fill(0); if (mitk::imv::IsInsideMask(pos, m_InterpolateMasks, m_MaskInterpolator) && !mitk::imv::IsInsideMask(pos, m_InterpolateMasks, m_StopInterpolator)) direction = m_TrackingHandler->ProposeDirection(pos, olddirs, oldIndex); // get direction proposal at current streamline position else return direction; vnl_vector_fixed olddir = olddirs.back(); std::vector< vnl_vector_fixed > probeVecs = CreateDirections(m_NumberOfSamples); itk::Point sample_pos; int alternatives = 1; int stop_votes = 0; int possible_stop_votes = 0; for (unsigned int i=0; i d; bool is_stop_voter = false; if (m_Random && m_RandomSampling) { d[0] = m_TrackingHandler->GetRandDouble(-0.5, 0.5); d[1] = m_TrackingHandler->GetRandDouble(-0.5, 0.5); d[2] = m_TrackingHandler->GetRandDouble(-0.5, 0.5); d.normalize(); d *= m_TrackingHandler->GetRandDouble(0,m_SamplingDistance); } else { d = probeVecs.at(i); float dot = dot_product(d, olddir); if (m_UseStopVotes && dot>0.7) { is_stop_voter = true; possible_stop_votes++; } else if (m_OnlyForwardSamples && dot<0) continue; d *= m_SamplingDistance; } sample_pos[0] = pos[0] + d[0]; sample_pos[1] = pos[1] + d[1]; sample_pos[2] = pos[2] + d[2]; vnl_vector_fixed tempDir; tempDir.fill(0.0); if (mitk::imv::IsInsideMask(sample_pos, m_InterpolateMasks, m_MaskInterpolator)) tempDir = m_TrackingHandler->ProposeDirection(sample_pos, olddirs, oldIndex); // sample neighborhood if (tempDir.magnitude()>mitk::eps) { direction += tempDir; if(m_DemoMode) m_SamplingPointset->InsertPoint(i, sample_pos); } else if (m_AvoidStop && olddir.magnitude()>0.5) // out of white matter { if (is_stop_voter) stop_votes++; if (m_DemoMode) m_StopVotePointset->InsertPoint(i, sample_pos); float dot = dot_product(d, olddir); if (dot >= 0.0) // in front of plane defined by pos and olddir d = -d + 2*dot*olddir; // reflect else d = -d; // invert // look a bit further into the other direction sample_pos[0] = pos[0] + d[0]; sample_pos[1] = pos[1] + d[1]; sample_pos[2] = pos[2] + d[2]; alternatives++; vnl_vector_fixed tempDir; tempDir.fill(0.0); if (mitk::imv::IsInsideMask(sample_pos, m_InterpolateMasks, m_MaskInterpolator)) tempDir = m_TrackingHandler->ProposeDirection(sample_pos, olddirs, oldIndex); // sample neighborhood if (tempDir.magnitude()>mitk::eps) // are we back in the white matter? { direction += d * m_DeflectionMod; // go into the direction of the white matter direction += tempDir; // go into the direction of the white matter direction at this location if(m_DemoMode) m_AlternativePointset->InsertPoint(alternatives, sample_pos); } else { if (m_DemoMode) m_StopVotePointset->InsertPoint(i, sample_pos); } } else { if (m_DemoMode) m_StopVotePointset->InsertPoint(i, sample_pos); if (is_stop_voter) stop_votes++; } } if (direction.magnitude()>0.001 && (possible_stop_votes==0 || (float)stop_votes/possible_stop_votes<0.5) ) direction.normalize(); else direction.fill(0); return direction; } -float StreamlineTrackingFilter::FollowStreamline(itk::Point pos, vnl_vector_fixed dir, FiberType* fib, DirectionContainer* container, float tractLength, bool front) +float StreamlineTrackingFilter::FollowStreamline(itk::Point pos, vnl_vector_fixed dir, FiberType* fib, DirectionContainer* container, float tractLength, bool front, bool &exclude) { vnl_vector_fixed zero_dir; zero_dir.fill(0.0); std::deque< vnl_vector_fixed > last_dirs; for (unsigned int i=0; i oldIndex; m_TrackingHandler->WorldToIndex(pos, oldIndex); // get new position CalculateNewPosition(pos, dir); - // is new position inside of image and mask - if (m_AbortTracking) // if not end streamline + if (m_ExclusionRegions.IsNotNull() && mitk::imv::IsInsideMask(pos, m_InterpolateMasks, m_ExclusionInterpolator)) + { + exclude = true; + return tractLength; + } + + if (m_AbortTracking) return tractLength; // if yes, add new point to streamline dir.normalize(); if (front) { fib->push_front(pos); container->push_front(dir); } else { fib->push_back(pos); container->push_back(dir); } tractLength += m_StepSize; if (m_LoopCheck>=0 && CheckCurvature(container, front)>m_LoopCheck) return tractLength; if (tractLength>m_MaxTractLength) return tractLength; if (m_DemoMode && !m_UseOutputProbabilityMap) // CHECK: warum sind die samplingpunkte der streamline in der visualisierung immer einen schritt voras? { #pragma omp critical { m_BuildFibersReady++; m_Tractogram.push_back(*fib); BuildFibers(true); m_Stop = true; while (m_Stop){ } } } last_dirs.push_back(dir); if (last_dirs.size()>m_NumPreviousDirections) last_dirs.pop_front(); dir = GetNewDirection(pos, last_dirs, oldIndex); while (m_PauseTracking){} if (dir.magnitude()<0.0001) return tractLength; } return tractLength; } float StreamlineTrackingFilter::CheckCurvature(DirectionContainer* fib, bool front) { if (fib->size()<8) return 0; float m_Distance = std::max(m_MinVoxelSize*4, m_StepSize*8); float dist = 0; std::vector< vnl_vector_fixed< float, 3 > > vectors; vnl_vector_fixed< float, 3 > meanV; meanV.fill(0); float dev = 0; if (front) { int c = 0; while(distsize()-1) { dist += m_StepSize; vnl_vector_fixed< float, 3 > v = fib->at(c); vectors.push_back(v); meanV += v; c++; } } else { int c = fib->size()-1; while(dist=0) { dist += m_StepSize; vnl_vector_fixed< float, 3 > v = fib->at(c); vectors.push_back(v); meanV += v; c--; } } meanV.normalize(); for (unsigned int c=0; c1.0) angle = 1.0; dev += acos(angle)*180/M_PI; } if (vectors.size()>0) dev /= vectors.size(); return dev; } void StreamlineTrackingFilter::GetSeedPointsFromSeedImage() { MITK_INFO << "StreamlineTracking - Calculating seed points."; m_SeedPoints.clear(); typedef ImageRegionConstIterator< ItkFloatImgType > MaskIteratorType; MaskIteratorType sit(m_SeedImage, m_SeedImage->GetLargestPossibleRegion()); sit.GoToBegin(); while (!sit.IsAtEnd()) { if (sit.Value()>0) { ItkFloatImgType::IndexType index = sit.GetIndex(); itk::ContinuousIndex start; start[0] = index[0]; start[1] = index[1]; start[2] = index[2]; itk::Point worldPos; m_SeedImage->TransformContinuousIndexToPhysicalPoint(start, worldPos); if ( mitk::imv::IsInsideMask(worldPos, m_InterpolateMasks, m_MaskInterpolator) ) { m_SeedPoints.push_back(worldPos); for (int s = 1; s < m_SeedsPerVoxel; s++) { start[0] = index[0] + m_TrackingHandler->GetRandDouble(-0.5, 0.5); start[1] = index[1] + m_TrackingHandler->GetRandDouble(-0.5, 0.5); start[2] = index[2] + m_TrackingHandler->GetRandDouble(-0.5, 0.5); itk::Point worldPos; m_SeedImage->TransformContinuousIndexToPhysicalPoint(start, worldPos); m_SeedPoints.push_back(worldPos); } } } ++sit; } } void StreamlineTrackingFilter::GenerateData() { this->BeforeTracking(); if (m_Random) std::random_shuffle(m_SeedPoints.begin(), m_SeedPoints.end()); m_CurrentTracts = 0; int num_seeds = m_SeedPoints.size(); itk::Index<3> zeroIndex; zeroIndex.Fill(0); m_Progress = 0; int i = 0; int print_interval = num_seeds/100; if (print_interval<100) m_Verbose=false; #pragma omp parallel while (i=num_seeds || m_StopTracking) continue; else if (m_Verbose && i%print_interval==0) #pragma omp critical { m_Progress += print_interval; std::cout << " \r"; if (m_MaxNumTracts>0) std::cout << "Tried: " << m_Progress << "/" << num_seeds << " | Accepted: " << m_CurrentTracts << "/" << m_MaxNumTracts << '\r'; else std::cout << "Tried: " << m_Progress << "/" << num_seeds << " | Accepted: " << m_CurrentTracts << '\r'; cout.flush(); } const itk::Point worldPos = m_SeedPoints.at(temp_i); for (unsigned int trials=0; trials dir; dir.fill(0.0); std::deque< vnl_vector_fixed > olddirs; while (olddirs.size() gm_start_dir; - // if (m_ControlGmEndings) - // { - // gm_start_dir[0] = m_GmStubs[temp_i][1][0] - m_GmStubs[temp_i][0][0]; - // gm_start_dir[1] = m_GmStubs[temp_i][1][1] - m_GmStubs[temp_i][0][1]; - // gm_start_dir[2] = m_GmStubs[temp_i][1][2] - m_GmStubs[temp_i][0][2]; - // gm_start_dir.normalize(); - // olddirs.pop_back(); - // olddirs.push_back(gm_start_dir); - // } - if (mitk::imv::IsInsideMask(worldPos, m_InterpolateMasks, m_MaskInterpolator)) dir = m_TrackingHandler->ProposeDirection(worldPos, olddirs, zeroIndex); + bool exclude = false; + if (m_ExclusionRegions.IsNotNull() && mitk::imv::IsInsideMask(worldPos, m_InterpolateMasks, m_ExclusionInterpolator)) + exclude = true; + bool success = false; - if (dir.magnitude()>0.0001) + if (dir.magnitude()>0.0001 && !exclude) { - /// START DIR - // if (m_ControlGmEndings) - // { - // float a = dot_product(gm_start_dir, dir); - // if (a<0) - // dir = -dir; - // } - // forward tracking - tractLength = FollowStreamline(worldPos, dir, &fib, &direction_container, 0, false); + tractLength = FollowStreamline(worldPos, dir, &fib, &direction_container, 0, false, exclude); fib.push_front(worldPos); - // backward tracking (only if we don't explicitely start in the GM) - tractLength = FollowStreamline(worldPos, -dir, &fib, &direction_container, tractLength, true); + // backward tracking + if (!exclude) + tractLength = FollowStreamline(worldPos, -dir, &fib, &direction_container, tractLength, true, exclude); counter = fib.size(); - if (tractLength>=m_MinTractLength && counter>=2) + if (tractLength>=m_MinTractLength && counter>=2 && !exclude) { #pragma omp critical if ( IsValidFiber(&fib) ) { if (!m_StopTracking) { if (!m_UseOutputProbabilityMap) m_Tractogram.push_back(fib); else FiberToProbmap(&fib); m_CurrentTracts++; success = true; } if (m_MaxNumTracts > 0 && m_CurrentTracts>=static_cast(m_MaxNumTracts)) { if (!m_StopTracking) { std::cout << " \r"; MITK_INFO << "Reconstructed maximum number of tracts (" << m_CurrentTracts << "). Stopping tractography."; } m_StopTracking = true; } } } } if (success || m_TrackingHandler->GetMode()!=mitk::TrackingDataHandler::PROBABILISTIC) break; // we only try one seed point multiple times if we use a probabilistic tracker and have not found a valid streamline yet }// trials per seed }// seed points this->AfterTracking(); } bool StreamlineTrackingFilter::IsValidFiber(FiberType* fib) { if (m_EndpointConstraint==EndpointConstraints::NONE) { return true; } else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) && mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint EPS_IN_TARGET chosen!"; } else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_TARGET_LABELDIFF) { if (m_TargetImageSet) { float v1 = mitk::imv::GetImageValue(fib->front(), false, m_TargetInterpolator); float v2 = mitk::imv::GetImageValue(fib->back(), false, m_TargetInterpolator); if ( v1>0.0 && v2>0.0 && v1!=v2 ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint EPS_IN_TARGET_LABELDIFF chosen!"; } else if (m_EndpointConstraint==EndpointConstraints::EPS_IN_SEED_AND_TARGET) { if (m_TargetImageSet && m_SeedImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_SeedInterpolator) && mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) ) return true; if ( mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_SeedInterpolator) && mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target or seed image set but endpoint constraint EPS_IN_SEED_AND_TARGET chosen!"; } else if (m_EndpointConstraint==EndpointConstraints::MIN_ONE_EP_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) || mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint MIN_ONE_EP_IN_TARGET chosen!"; } else if (m_EndpointConstraint==EndpointConstraints::ONE_EP_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) && !mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) ) return true; if ( !mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) && mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint ONE_EP_IN_TARGET chosen!"; } else if (m_EndpointConstraint==EndpointConstraints::NO_EP_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_InterpolateMasks, m_TargetInterpolator) || mitk::imv::IsInsideMask(fib->back(), m_InterpolateMasks, m_TargetInterpolator) ) return false; return true; } else mitkThrow() << "No target image set but endpoint constraint NO_EP_IN_TARGET chosen!"; } return true; } void StreamlineTrackingFilter::FiberToProbmap(FiberType* fib) { ItkDoubleImgType::IndexType last_idx; last_idx.Fill(0); for (auto p : *fib) { ItkDoubleImgType::IndexType idx; m_OutputProbabilityMap->TransformPhysicalPointToIndex(p, idx); if (idx != last_idx) { if (m_OutputProbabilityMap->GetLargestPossibleRegion().IsInside(idx)) m_OutputProbabilityMap->SetPixel(idx, m_OutputProbabilityMap->GetPixel(idx)+1); last_idx = idx; } } } void StreamlineTrackingFilter::BuildFibers(bool check) { if (m_BuildFibersReady::New(); vtkSmartPointer vNewLines = vtkSmartPointer::New(); vtkSmartPointer vNewPoints = vtkSmartPointer::New(); for (unsigned int i=0; i container = vtkSmartPointer::New(); FiberType fib = m_Tractogram.at(i); for (FiberType::iterator it = fib.begin(); it!=fib.end(); ++it) { vtkIdType id = vNewPoints->InsertNextPoint((*it).GetDataPointer()); container->GetPointIds()->InsertNextId(id); } vNewLines->InsertNextCell(container); } if (check) for (int i=0; iSetPoints(vNewPoints); m_FiberPolyData->SetLines(vNewLines); m_BuildFibersFinished = true; } void StreamlineTrackingFilter::AfterTracking() { if (m_Verbose) std::cout << " \r"; if (!m_UseOutputProbabilityMap) { MITK_INFO << "Reconstructed " << m_Tractogram.size() << " fibers."; MITK_INFO << "Generating polydata "; BuildFibers(false); } else { itk::RescaleIntensityImageFilter< ItkDoubleImgType, ItkDoubleImgType >::Pointer filter = itk::RescaleIntensityImageFilter< ItkDoubleImgType, ItkDoubleImgType >::New(); filter->SetInput(m_OutputProbabilityMap); filter->SetOutputMaximum(1.0); filter->SetOutputMinimum(0.0); filter->Update(); m_OutputProbabilityMap = filter->GetOutput(); } MITK_INFO << "done"; m_EndTime = std::chrono::system_clock::now(); std::chrono::hours hh = std::chrono::duration_cast(m_EndTime - m_StartTime); std::chrono::minutes mm = std::chrono::duration_cast(m_EndTime - m_StartTime); std::chrono::seconds ss = std::chrono::duration_cast(m_EndTime - m_StartTime); mm %= 60; ss %= 60; MITK_INFO << "Tracking took " << hh.count() << "h, " << mm.count() << "m and " << ss.count() << "s"; m_SeedPoints.clear(); } void StreamlineTrackingFilter::SetDicomProperties(mitk::FiberBundle::Pointer fib) { std::string model_code_value = "-"; std::string model_code_meaning = "-"; std::string algo_code_value = "-"; std::string algo_code_meaning = "-"; if (m_TrackingHandler->GetMode()==mitk::TrackingDataHandler::DETERMINISTIC && dynamic_cast(m_TrackingHandler) && !m_TrackingHandler->GetInterpolate()) { algo_code_value = "sup181_ee04"; algo_code_meaning = "FACT"; } else if (m_TrackingHandler->GetMode()==mitk::TrackingDataHandler::DETERMINISTIC) { algo_code_value = "sup181_ee01"; algo_code_meaning = "Deterministic"; } else if (m_TrackingHandler->GetMode()==mitk::TrackingDataHandler::PROBABILISTIC) { algo_code_value = "sup181_ee02"; algo_code_meaning = "Probabilistic"; } if (dynamic_cast(m_TrackingHandler) || (dynamic_cast(m_TrackingHandler) && dynamic_cast(m_TrackingHandler)->GetIsOdfFromTensor() ) ) { if ( dynamic_cast(m_TrackingHandler) && dynamic_cast(m_TrackingHandler)->GetNumTensorImages()>1 ) { model_code_value = "sup181_bb02"; model_code_meaning = "Multi Tensor"; } else { model_code_value = "sup181_bb01"; model_code_meaning = "Single Tensor"; } } else if (dynamic_cast*>(m_TrackingHandler) || dynamic_cast*>(m_TrackingHandler)) { model_code_value = "sup181_bb03"; model_code_meaning = "Model Free"; } else if (dynamic_cast(m_TrackingHandler)) { model_code_value = "-"; model_code_meaning = "ODF"; } else if (dynamic_cast(m_TrackingHandler)) { model_code_value = "-"; model_code_meaning = "Peaks"; } fib->SetProperty("DICOM.anatomy.value", mitk::StringProperty::New("T-A0095")); fib->SetProperty("DICOM.anatomy.meaning", mitk::StringProperty::New("White matter of brain and spinal cord")); fib->SetProperty("DICOM.algo_code.value", mitk::StringProperty::New(algo_code_value)); fib->SetProperty("DICOM.algo_code.meaning", mitk::StringProperty::New(algo_code_meaning)); fib->SetProperty("DICOM.model_code.value", mitk::StringProperty::New(model_code_value)); fib->SetProperty("DICOM.model_code.meaning", mitk::StringProperty::New(model_code_meaning)); } } diff --git a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.h b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.h index 45abc80170..e3f6e8f3c7 100644 --- a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.h +++ b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.h @@ -1,243 +1,246 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #ifndef __itkMLBSTrackingFilter_h_ #define __itkMLBSTrackingFilter_h_ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include namespace itk{ /** * \brief Performs streamline tracking on the input image. Depending on the tracking handler this can be a tensor, peak or machine learning based tracking. */ class MITKFIBERTRACKING_EXPORT StreamlineTrackingFilter : public ProcessObject { public: enum EndpointConstraints { NONE, ///< No constraints on endpoint locations EPS_IN_TARGET, ///< Both EPs are required to be located in the target image EPS_IN_TARGET_LABELDIFF, ///< Both EPs are required to be located in the target image and the image values at the respective position needs to be distinct EPS_IN_SEED_AND_TARGET, ///< One EP is required to be located in the seed image and one in the target image MIN_ONE_EP_IN_TARGET, ///< At least one EP is required to be located in the target image ONE_EP_IN_TARGET, ///< Exactly one EP is required to be located in the target image NO_EP_IN_TARGET ///< No EP is allowed to be located in the target image }; typedef StreamlineTrackingFilter Self; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; typedef ProcessObject Superclass; /** Method for creation through the object factory. */ itkFactorylessNewMacro(Self) itkCloneMacro(Self) /** Runtime information support. */ itkTypeMacro(MLBSTrackingFilter, ImageToImageFilter) typedef itk::Image ItkUcharImgType; typedef itk::Image ItkUintImgType; typedef itk::Image ItkDoubleImgType; typedef itk::Image ItkFloatImgType; typedef vtkSmartPointer< vtkPolyData > PolyDataType; typedef std::deque< vnl_vector_fixed > DirectionContainer; typedef std::deque< itk::Point > FiberType; typedef std::vector< FiberType > BundleType; volatile bool m_PauseTracking; bool m_AbortTracking; bool m_BuildFibersFinished; int m_BuildFibersReady; volatile bool m_Stop; mitk::PointSet::Pointer m_SamplingPointset; mitk::PointSet::Pointer m_StopVotePointset; mitk::PointSet::Pointer m_AlternativePointset; void SetStepSize(float v) ///< Integration step size in voxels, default is 0.5 * voxel { m_StepSizeVox = v; } void SetAngularThreshold(float v) ///< Angular threshold per step (in degree), default is 90deg x stepsize { m_AngularThresholdDeg = v; } void SetSamplingDistance(float v) ///< Maximum distance of sampling points in voxels, default is 0.25 * voxel { m_SamplingDistanceVox = v; } void SetDicomProperties(mitk::FiberBundle::Pointer fib); itkGetMacro( OutputProbabilityMap, ItkDoubleImgType::Pointer) ///< Output probability map itkGetMacro( FiberPolyData, PolyDataType ) ///< Output fibers itkGetMacro( UseOutputProbabilityMap, bool) itkGetMacro( MinVoxelSize, float) itkGetMacro( EndpointConstraint, EndpointConstraints) itkSetMacro( SeedImage, ItkFloatImgType::Pointer) ///< Seeds are only placed inside of this mask. itkSetMacro( MaskImage, ItkFloatImgType::Pointer) ///< Tracking is only performed inside of this mask image. + itkSetMacro( ExclusionRegions, ItkFloatImgType::Pointer)///< Fibers passing any of the ROIs in this image are discarded. itkSetMacro( SeedsPerVoxel, int) ///< One seed placed in the center of each voxel or multiple seeds randomly placed inside each voxel. itkSetMacro( MinTractLength, float ) ///< Shorter tracts are discarded. itkSetMacro( MaxTractLength, float ) ///< Streamline progression stops if tract is longer than specified. itkSetMacro( EndpointConstraint, EndpointConstraints) ///< Determines what fibers are accepted based on their endpoint location itkSetMacro( UseStopVotes, bool ) ///< Frontal sampling points can vote for stopping the streamline even if the remaining sampling points keep pushing itkSetMacro( OnlyForwardSamples, bool ) ///< Don't use sampling points behind the current position in progression direction itkSetMacro( DeflectionMod, float ) ///< Deflection distance modifier itkSetMacro( StoppingRegions, ItkFloatImgType::Pointer) ///< Streamlines entering a stopping region will stop immediately itkSetMacro( TargetRegions, ItkFloatImgType::Pointer) ///< Only streamline starting and ending in this mask are retained itkSetMacro( DemoMode, bool ) itkSetMacro( NumberOfSamples, unsigned int ) ///< Number of neighborhood sampling points itkSetMacro( LoopCheck, float ) ///< Checks fiber curvature (angular deviation across 5mm) is larger than 30°. If yes, the streamline progression is stopped. itkSetMacro( AvoidStop, bool ) ///< Use additional sampling points to avoid premature streamline termination itkSetMacro( RandomSampling, bool ) ///< If true, the sampling points are distributed randomly around the current position, not sphericall in the specified sampling distance. itkSetMacro( NumPreviousDirections, unsigned int ) ///< How many "old" steps do we want to consider in our decision where to go next? itkSetMacro( MaxNumTracts, int ) ///< Tracking is stopped if the maximum number of tracts is exceeded itkSetMacro( Random, bool ) ///< If true, seedpoints are shuffled randomly before tracking itkSetMacro( Verbose, bool ) ///< If true, output tracking progress (might be slower) itkSetMacro( UseOutputProbabilityMap, bool) ///< If true, no tractogram but a probability map is created as output. itkSetMacro( StopTracking, bool ) itkSetMacro( InterpolateMasks, bool ) itkSetMacro( TrialsPerSeed, unsigned int ) ///< When using probabilistic tractography, each seed point is used N times until a valid streamline that is compliant with all thresholds etc. is found ///< Use manually defined points in physical space as seed points instead of seed image void SetSeedPoints( const std::vector< itk::Point >& sP) { m_SeedPoints = sP; } void SetTrackingHandler( mitk::TrackingDataHandler* h ) ///< { m_TrackingHandler = h; } virtual void Update() override{ this->GenerateData(); } std::string GetStatusText(); protected: void GenerateData() override; StreamlineTrackingFilter(); ~StreamlineTrackingFilter() {} bool IsValidFiber(FiberType* fib); ///< Check endpoints void FiberToProbmap(FiberType* fib); void GetSeedPointsFromSeedImage(); void CalculateNewPosition(itk::Point& pos, vnl_vector_fixed& dir); ///< Calculate next integration step. - float FollowStreamline(itk::Point start_pos, vnl_vector_fixed dir, FiberType* fib, DirectionContainer* container, float tractLength, bool front); ///< Start streamline in one direction. + float FollowStreamline(itk::Point start_pos, vnl_vector_fixed dir, FiberType* fib, DirectionContainer* container, float tractLength, bool front, bool& exclude); ///< Start streamline in one direction. vnl_vector_fixed GetNewDirection(itk::Point& pos, std::deque< vnl_vector_fixed >& olddirs, itk::Index<3>& oldIndex); ///< Determine new direction by sample voting at the current position taking the last progression direction into account. std::vector< vnl_vector_fixed > CreateDirections(int NPoints); void BeforeTracking(); void AfterTracking(); PolyDataType m_FiberPolyData; vtkSmartPointer m_Points; vtkSmartPointer m_Cells; BundleType m_Tractogram; BundleType m_GmStubs; ItkFloatImgType::Pointer m_StoppingRegions; ItkFloatImgType::Pointer m_TargetRegions; ItkFloatImgType::Pointer m_SeedImage; ItkFloatImgType::Pointer m_MaskImage; + ItkFloatImgType::Pointer m_ExclusionRegions; ItkDoubleImgType::Pointer m_OutputProbabilityMap; float m_MinVoxelSize; float m_AngularThresholdDeg; float m_StepSizeVox; float m_SamplingDistanceVox; float m_AngularThreshold; float m_StepSize; int m_MaxLength; float m_MinTractLength; float m_MaxTractLength; int m_SeedsPerVoxel; bool m_AvoidStop; bool m_RandomSampling; float m_SamplingDistance; float m_DeflectionMod; bool m_OnlyForwardSamples; bool m_UseStopVotes; unsigned int m_NumberOfSamples; unsigned int m_NumPreviousDirections; int m_MaxNumTracts; bool m_Verbose; float m_LoopCheck; bool m_DemoMode; bool m_Random; bool m_UseOutputProbabilityMap; std::vector< itk::Point > m_SeedPoints; unsigned int m_CurrentTracts; unsigned int m_Progress; bool m_StopTracking; bool m_InterpolateMasks; unsigned int m_TrialsPerSeed; EndpointConstraints m_EndpointConstraint; void BuildFibers(bool check); float CheckCurvature(DirectionContainer *fib, bool front); // decision forest mitk::TrackingDataHandler* m_TrackingHandler; std::vector< PolyDataType > m_PolyDataContainer; std::chrono::time_point m_StartTime; std::chrono::time_point m_EndTime; itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::Pointer m_MaskInterpolator; itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::Pointer m_StopInterpolator; itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::Pointer m_TargetInterpolator; itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::Pointer m_SeedInterpolator; + itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::Pointer m_ExclusionInterpolator; bool m_SeedImageSet; bool m_TargetImageSet; private: }; } //#ifndef ITK_MANUAL_INSTANTIATION //#include "itkMLBSTrackingFilter.cpp" //#endif #endif //__itkMLBSTrackingFilter_h_ diff --git a/Modules/DiffusionImaging/FiberTracking/cmdapps/Tractography/StreamlineTractography.cpp b/Modules/DiffusionImaging/FiberTracking/cmdapps/Tractography/StreamlineTractography.cpp index 31fdfb099e..9968c4979c 100755 --- a/Modules/DiffusionImaging/FiberTracking/cmdapps/Tractography/StreamlineTractography.cpp +++ b/Modules/DiffusionImaging/FiberTracking/cmdapps/Tractography/StreamlineTractography.cpp @@ -1,505 +1,520 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #define _USE_MATH_DEFINES #include const int numOdfSamples = 200; typedef itk::Image< itk::Vector< float, numOdfSamples > , 3 > SampledShImageType; /*! \brief */ int main(int argc, char* argv[]) { mitkCommandLineParser parser; parser.setTitle("Streamline Tractography"); parser.setCategory("Fiber Tracking and Processing Methods"); parser.setDescription("Perform streamline tractography"); parser.setContributor("MIC"); // parameters fo all methods parser.setArgumentPrefix("--", "-"); parser.beginGroup("1. Mandatory arguments:"); parser.addArgument("input", "i", mitkCommandLineParser::StringList, "Input:", "input image (multiple possible for 'DetTensor' algorithm)", us::Any(), false); parser.addArgument("algorithm", "a", mitkCommandLineParser::String, "Algorithm:", "which algorithm to use (Peaks, DetTensor, ProbTensor, DetODF, ProbODF, DetRF, ProbRF)", us::Any(), false); parser.addArgument("out", "o", mitkCommandLineParser::OutputDirectory, "Output:", "output fiberbundle/probability map", us::Any(), false); parser.endGroup(); parser.beginGroup("2. Seeding:"); parser.addArgument("seeds", "", mitkCommandLineParser::Int, "Seeds per voxel:", "number of seed points per voxel", 1); parser.addArgument("seed_image", "", mitkCommandLineParser::String, "Seed image:", "mask image defining seed voxels", us::Any()); parser.addArgument("trials_per_seed", "", mitkCommandLineParser::Int, "Max. trials per seed:", "try each seed N times until a valid streamline is obtained (only for probabilistic tractography)", 10); parser.addArgument("max_tracts", "", mitkCommandLineParser::Int, "Max. number of tracts:", "tractography is stopped if the reconstructed number of tracts is exceeded", -1); parser.endGroup(); parser.beginGroup("3. Tractography constraints:"); parser.addArgument("tracking_mask", "", mitkCommandLineParser::String, "Mask image:", "streamlines leaving the mask will stop immediately", us::Any()); - parser.addArgument("stop_image", "", mitkCommandLineParser::String, "Stop image:", "streamlines entering the mask will stop immediately", us::Any()); - parser.addArgument("target_image", "", mitkCommandLineParser::String, "Target image:", "effact depends on the chosen endpoint constraint (option ep_constraint)", us::Any()); + parser.addArgument("stop_image", "", mitkCommandLineParser::String, "Stop ROI image:", "streamlines entering the mask will stop immediately", us::Any()); + parser.addArgument("exclusion_image", "", mitkCommandLineParser::String, "Exclusion ROI image:", "streamlines entering the mask will be discarded", us::Any()); parser.addArgument("ep_constraint", "", mitkCommandLineParser::String, "Endpoint constraint:", "determines which fibers are accepted based on their endpoint location - options are NONE, EPS_IN_TARGET, EPS_IN_TARGET_LABELDIFF, EPS_IN_SEED_AND_TARGET, MIN_ONE_EP_IN_TARGET, ONE_EP_IN_TARGET and NO_EP_IN_TARGET", us::Any()); + parser.addArgument("target_image", "", mitkCommandLineParser::String, "Target ROI image:", "effact depends on the chosen endpoint constraint (option ep_constraint)", us::Any()); parser.endGroup(); parser.beginGroup("4. Streamline integration parameters:"); parser.addArgument("sharpen_odfs", "", mitkCommandLineParser::Bool, "SHarpen ODFs:", "if you are using dODF images as input, it is advisable to sharpen the ODFs (min-max normalize and raise to the power of 4). this is not necessary for CSD fODFs, since they are narurally much sharper."); parser.addArgument("cutoff", "", mitkCommandLineParser::Float, "Cutoff:", "set the FA, GFA or Peak amplitude cutoff for terminating tracks", 0.1); parser.addArgument("odf_cutoff", "", mitkCommandLineParser::Float, "ODF Cutoff:", "threshold on the ODF magnitude. this is useful in case of CSD fODF tractography.", 0.0); parser.addArgument("step_size", "", mitkCommandLineParser::Float, "Step size:", "step size (in voxels)", 0.5); parser.addArgument("min_tract_length", "", mitkCommandLineParser::Float, "Min. tract length:", "minimum fiber length (in mm)", 20); parser.addArgument("angular_threshold", "", mitkCommandLineParser::Float, "Angular threshold:", "angular threshold between two successive steps, (default: 90° * step_size, minimum 15°)"); parser.addArgument("loop_check", "", mitkCommandLineParser::Float, "Check for loops:", "threshold on angular stdev over the last 4 voxel lengths"); parser.endGroup(); parser.beginGroup("5. Neighborhood sampling:"); parser.addArgument("num_samples", "", mitkCommandLineParser::Int, "Num. neighborhood samples:", "number of neighborhood samples that are use to determine the next progression direction", 0); parser.addArgument("sampling_distance", "", mitkCommandLineParser::Float, "Sampling distance:", "distance of neighborhood sampling points (in voxels)", 0.25); parser.addArgument("use_stop_votes", "", mitkCommandLineParser::Bool, "Use stop votes:", "use stop votes"); parser.addArgument("use_only_forward_samples", "", mitkCommandLineParser::Bool, "Use only forward samples:", "use only forward samples"); parser.endGroup(); parser.beginGroup("6. Tensor tractography specific:"); parser.addArgument("tend_f", "", mitkCommandLineParser::Float, "Weight f", "weighting factor between first eigenvector (f=1 equals FACT tracking) and input vector dependent direction (f=0).", 1.0); parser.addArgument("tend_g", "", mitkCommandLineParser::Float, "Weight g", "weighting factor between input vector (g=0) and tensor deflection (g=1 equals TEND tracking)", 0.0); parser.endGroup(); parser.beginGroup("7. Random forest tractography specific:"); parser.addArgument("forest", "", mitkCommandLineParser::String, "Forest:", "input random forest (HDF5 file)", us::Any()); parser.addArgument("use_sh_features", "", mitkCommandLineParser::Bool, "Use SH features:", "use SH features"); parser.endGroup(); parser.beginGroup("8. Additional input:"); parser.addArgument("additional_images", "", mitkCommandLineParser::StringList, "Additional images:", "specify a list of float images that hold additional information (FA, GFA, additional features for RF tractography)", us::Any()); parser.endGroup(); parser.beginGroup("9. Misc:"); parser.addArgument("flip_x", "", mitkCommandLineParser::Bool, "Flip X:", "multiply x-coordinate of direction proposal by -1"); parser.addArgument("flip_y", "", mitkCommandLineParser::Bool, "Flip Y:", "multiply y-coordinate of direction proposal by -1"); parser.addArgument("flip_z", "", mitkCommandLineParser::Bool, "Flip Z:", "multiply z-coordinate of direction proposal by -1"); parser.addArgument("no_data_interpolation", "", mitkCommandLineParser::Bool, "Don't interpolate input data:", "don't interpolate input image values"); parser.addArgument("no_mask_interpolation", "", mitkCommandLineParser::Bool, "Don't interpolate masks:", "don't interpolate mask image values"); parser.addArgument("compress", "", mitkCommandLineParser::Float, "Compress:", "compress output fibers using the given error threshold (in mm)"); parser.endGroup(); std::map parsedArgs = parser.parseArguments(argc, argv); if (parsedArgs.size()==0) return EXIT_FAILURE; mitkCommandLineParser::StringContainerType input_files = us::any_cast(parsedArgs["input"]); std::string outFile = us::any_cast(parsedArgs["out"]); std::string algorithm = us::any_cast(parsedArgs["algorithm"]); bool sharpen_odfs = false; if (parsedArgs.count("sharpen_odfs")) sharpen_odfs = us::any_cast(parsedArgs["sharpen_odfs"]); bool interpolate = true; if (parsedArgs.count("no_data_interpolation")) interpolate = !us::any_cast(parsedArgs["no_data_interpolation"]); bool mask_interpolation = true; if (parsedArgs.count("no_mask_interpolation")) interpolate = !us::any_cast(parsedArgs["no_mask_interpolation"]); bool use_sh_features = false; if (parsedArgs.count("use_sh_features")) use_sh_features = us::any_cast(parsedArgs["use_sh_features"]); bool use_stop_votes = false; if (parsedArgs.count("use_stop_votes")) use_stop_votes = us::any_cast(parsedArgs["use_stop_votes"]); bool use_only_forward_samples = false; if (parsedArgs.count("use_only_forward_samples")) use_only_forward_samples = us::any_cast(parsedArgs["use_only_forward_samples"]); bool flip_x = false; if (parsedArgs.count("flip_x")) flip_x = us::any_cast(parsedArgs["flip_x"]); bool flip_y = false; if (parsedArgs.count("flip_y")) flip_y = us::any_cast(parsedArgs["flip_y"]); bool flip_z = false; if (parsedArgs.count("flip_z")) flip_z = us::any_cast(parsedArgs["flip_z"]); bool apply_image_rotation = false; if (parsedArgs.count("apply_image_rotation")) apply_image_rotation = us::any_cast(parsedArgs["apply_image_rotation"]); float compress = -1; if (parsedArgs.count("compress")) compress = us::any_cast(parsedArgs["compress"]); float min_tract_length = 20; if (parsedArgs.count("min_tract_length")) min_tract_length = us::any_cast(parsedArgs["min_tract_length"]); float loop_check = -1; if (parsedArgs.count("loop_check")) loop_check = us::any_cast(parsedArgs["loop_check"]); std::string forestFile; if (parsedArgs.count("forest")) forestFile = us::any_cast(parsedArgs["forest"]); std::string maskFile = ""; if (parsedArgs.count("tracking_mask")) maskFile = us::any_cast(parsedArgs["tracking_mask"]); std::string seedFile = ""; if (parsedArgs.count("seed_image")) seedFile = us::any_cast(parsedArgs["seed_image"]); std::string targetFile = ""; if (parsedArgs.count("target_image")) targetFile = us::any_cast(parsedArgs["target_image"]); + std::string exclusionFile = ""; + if (parsedArgs.count("exclusion_image")) + exclusionFile = us::any_cast(parsedArgs["exclusion_image"]); + std::string stopFile = ""; if (parsedArgs.count("stop_image")) stopFile = us::any_cast(parsedArgs["stop_image"]); std::string ep_constraint = "NONE"; if (parsedArgs.count("ep_constraint")) ep_constraint = us::any_cast(parsedArgs["ep_constraint"]); float cutoff = 0.1; if (parsedArgs.count("cutoff")) cutoff = us::any_cast(parsedArgs["cutoff"]); float odf_cutoff = 0.0; if (parsedArgs.count("odf_cutoff")) odf_cutoff = us::any_cast(parsedArgs["odf_cutoff"]); float stepsize = -1; if (parsedArgs.count("step_size")) stepsize = us::any_cast(parsedArgs["step_size"]); float sampling_distance = -1; if (parsedArgs.count("sampling_distance")) sampling_distance = us::any_cast(parsedArgs["sampling_distance"]); int num_samples = 0; if (parsedArgs.count("num_samples")) num_samples = us::any_cast(parsedArgs["num_samples"]); - int seeds = 1; + int num_seeds = 1; if (parsedArgs.count("seeds")) - seeds = us::any_cast(parsedArgs["seeds"]); + num_seeds = us::any_cast(parsedArgs["seeds"]); unsigned int trials_per_seed = 10; if (parsedArgs.count("trials_per_seed")) trials_per_seed = us::any_cast(parsedArgs["trials_per_seed"]); float tend_f = 1; if (parsedArgs.count("tend_f")) tend_f = us::any_cast(parsedArgs["tend_f"]); float tend_g = 0; if (parsedArgs.count("tend_g")) tend_g = us::any_cast(parsedArgs["tend_g"]); float angular_threshold = -1; if (parsedArgs.count("angular_threshold")) angular_threshold = us::any_cast(parsedArgs["angular_threshold"]); unsigned int max_tracts = -1; if (parsedArgs.count("max_tracts")) max_tracts = us::any_cast(parsedArgs["max_tracts"]); std::string ext = itksys::SystemTools::GetFilenameExtension(outFile); if (ext != ".fib" && ext != ".trk") { MITK_INFO << "Output file format not supported. Use one of .fib, .trk, .nii, .nii.gz, .nrrd"; return EXIT_FAILURE; } // LOAD DATASETS mitkCommandLineParser::StringContainerType addFiles; if (parsedArgs.count("additional_images")) addFiles = us::any_cast(parsedArgs["additional_images"]); typedef itk::Image ItkFloatImgType; MITK_INFO << "loading input"; std::vector< mitk::Image::Pointer > input_images; for (unsigned int i=0; i(mitk::IOUtil::Load(input_files.at(i))[0].GetPointer()); input_images.push_back(mitkImage); } - ItkFloatImgType::Pointer mask; + ItkFloatImgType::Pointer mask = nullptr; if (!maskFile.empty()) { MITK_INFO << "loading mask image"; mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(maskFile)[0].GetPointer()); mask = ItkFloatImgType::New(); mitk::CastToItkImage(img, mask); } - ItkFloatImgType::Pointer seed; + ItkFloatImgType::Pointer seed = nullptr; if (!seedFile.empty()) { - MITK_INFO << "loading seed image"; + MITK_INFO << "loading seed ROI image"; mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(seedFile)[0].GetPointer()); seed = ItkFloatImgType::New(); mitk::CastToItkImage(img, seed); } - ItkFloatImgType::Pointer stop; + ItkFloatImgType::Pointer stop = nullptr; if (!stopFile.empty()) { - MITK_INFO << "loading stop image"; + MITK_INFO << "loading stop ROI image"; mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(stopFile)[0].GetPointer()); stop = ItkFloatImgType::New(); mitk::CastToItkImage(img, stop); } - ItkFloatImgType::Pointer target; + ItkFloatImgType::Pointer target = nullptr; if (!targetFile.empty()) { - MITK_INFO << "loading target image"; + MITK_INFO << "loading target ROI image"; mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(targetFile)[0].GetPointer()); target = ItkFloatImgType::New(); mitk::CastToItkImage(img, target); } + ItkFloatImgType::Pointer exclusion = nullptr; + if (!exclusionFile.empty()) + { + MITK_INFO << "loading exclusion ROI image"; + mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(exclusionFile)[0].GetPointer()); + exclusion = ItkFloatImgType::New(); + mitk::CastToItkImage(img, exclusion); + } + MITK_INFO << "loading additional images"; std::vector< std::vector< ItkFloatImgType::Pointer > > addImages; addImages.push_back(std::vector< ItkFloatImgType::Pointer >()); for (auto file : addFiles) { mitk::Image::Pointer img = dynamic_cast(mitk::IOUtil::Load(file)[0].GetPointer()); ItkFloatImgType::Pointer itkimg = ItkFloatImgType::New(); mitk::CastToItkImage(img, itkimg); addImages.at(0).push_back(itkimg); } // ////////////////////////////////////////////////////////////////// // omp_set_num_threads(1); if (algorithm == "ProbTensor") { typedef mitk::ImageToItk< mitk::TrackingHandlerTensor::ItkTensorImageType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(input_images.at(0)); caster->Update(); mitk::TrackingHandlerTensor::ItkTensorImageType::Pointer itkTensorImg = caster->GetOutput(); typedef itk::TensorImageToOdfImageFilter< float, float > FilterType; FilterType::Pointer filter = FilterType::New(); filter->SetInput( itkTensorImg ); filter->Update(); mitk::Image::Pointer image = mitk::Image::New(); FilterType::OutputImageType::Pointer outimg = filter->GetOutput(); image->InitializeByItk( outimg.GetPointer() ); image->SetVolume( outimg->GetBufferPointer() ); input_images.clear(); input_images.push_back(image); sharpen_odfs = true; odf_cutoff = 0; } typedef itk::StreamlineTrackingFilter TrackerType; TrackerType::Pointer tracker = TrackerType::New(); mitk::TrackingDataHandler* handler; if (algorithm == "DetRF" || algorithm == "ProbRF") { mitk::TractographyForest::Pointer forest = dynamic_cast(mitk::IOUtil::Load(forestFile)[0].GetPointer()); if (forest.IsNull()) mitkThrow() << "Forest file " << forestFile << " could not be read."; if (use_sh_features) { handler = new mitk::TrackingHandlerRandomForest<6,28>(); dynamic_cast*>(handler)->SetForest(forest); dynamic_cast*>(handler)->AddDwi(input_images.at(0)); dynamic_cast*>(handler)->SetAdditionalFeatureImages(addImages); } else { handler = new mitk::TrackingHandlerRandomForest<6,100>(); dynamic_cast*>(handler)->SetForest(forest); dynamic_cast*>(handler)->AddDwi(input_images.at(0)); dynamic_cast*>(handler)->SetAdditionalFeatureImages(addImages); } if (algorithm == "ProbRF") handler->SetMode(mitk::TrackingDataHandler::MODE::PROBABILISTIC); } else if (algorithm == "Peaks") { handler = new mitk::TrackingHandlerPeaks(); typedef mitk::ImageToItk< mitk::TrackingHandlerPeaks::PeakImgType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(input_images.at(0)); caster->Update(); mitk::TrackingHandlerPeaks::PeakImgType::Pointer itkImg = caster->GetOutput(); dynamic_cast(handler)->SetPeakImage(itkImg); dynamic_cast(handler)->SetApplyDirectionMatrix(apply_image_rotation); dynamic_cast(handler)->SetPeakThreshold(cutoff); } else if (algorithm == "DetTensor") { handler = new mitk::TrackingHandlerTensor(); for (auto input_image : input_images) { typedef mitk::ImageToItk< mitk::TrackingHandlerTensor::ItkTensorImageType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(input_image); caster->Update(); mitk::TrackingHandlerTensor::ItkTensorImageType::ConstPointer itkImg = caster->GetOutput(); dynamic_cast(handler)->AddTensorImage(itkImg); } dynamic_cast(handler)->SetFaThreshold(cutoff); dynamic_cast(handler)->SetF(tend_f); dynamic_cast(handler)->SetG(tend_g); if (addImages.at(0).size()>0) dynamic_cast(handler)->SetFaImage(addImages.at(0).at(0)); } else if (algorithm == "DetODF" || algorithm == "ProbODF" || algorithm == "ProbTensor") { handler = new mitk::TrackingHandlerOdf(); typedef mitk::ImageToItk< mitk::TrackingHandlerOdf::ItkOdfImageType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(input_images.at(0)); caster->Update(); mitk::TrackingHandlerOdf::ItkOdfImageType::Pointer itkImg = caster->GetOutput(); dynamic_cast(handler)->SetOdfImage(itkImg); dynamic_cast(handler)->SetGfaThreshold(cutoff); dynamic_cast(handler)->SetOdfThreshold(odf_cutoff); dynamic_cast(handler)->SetSharpenOdfs(sharpen_odfs); if (algorithm == "ProbODF" || algorithm == "ProbTensor") dynamic_cast(handler)->SetMode(mitk::TrackingHandlerOdf::MODE::PROBABILISTIC); if (algorithm == "ProbTensor") dynamic_cast(handler)->SetIsOdfFromTensor(true); if (addImages.at(0).size()>0) dynamic_cast(handler)->SetGfaImage(addImages.at(0).at(0)); } else { MITK_INFO << "Unknown tractography algorithm (" + algorithm+"). Known types are Peaks, DetTensor, ProbTensor, DetODF, ProbODF, DetRF, ProbRF."; return EXIT_FAILURE; } handler->SetInterpolate(interpolate); handler->SetFlipX(flip_x); handler->SetFlipY(flip_y); handler->SetFlipZ(flip_z); if (ep_constraint=="NONE") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::NONE); else if (ep_constraint=="EPS_IN_TARGET") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET); else if (ep_constraint=="EPS_IN_TARGET_LABELDIFF") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET_LABELDIFF); else if (ep_constraint=="EPS_IN_SEED_AND_TARGET") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_SEED_AND_TARGET); else if (ep_constraint=="MIN_ONE_EP_IN_TARGET") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::MIN_ONE_EP_IN_TARGET); else if (ep_constraint=="ONE_EP_IN_TARGET") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::ONE_EP_IN_TARGET); else if (ep_constraint=="NO_EP_IN_TARGET") tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::NO_EP_IN_TARGET); MITK_INFO << "Tractography algorithm: " << algorithm; tracker->SetInterpolateMasks(mask_interpolation); tracker->SetNumberOfSamples(num_samples); tracker->SetAngularThreshold(angular_threshold); tracker->SetMaskImage(mask); tracker->SetSeedImage(seed); tracker->SetStoppingRegions(stop); tracker->SetTargetRegions(target); - tracker->SetSeedsPerVoxel(seeds); + tracker->SetExclusionRegions(exclusion); + tracker->SetSeedsPerVoxel(num_seeds); tracker->SetStepSize(stepsize); tracker->SetSamplingDistance(sampling_distance); tracker->SetUseStopVotes(use_stop_votes); tracker->SetOnlyForwardSamples(use_only_forward_samples); tracker->SetLoopCheck(loop_check); tracker->SetMaxNumTracts(max_tracts); tracker->SetTrialsPerSeed(trials_per_seed); tracker->SetTrackingHandler(handler); if (ext != ".fib" && ext != ".trk") tracker->SetUseOutputProbabilityMap(true); tracker->SetMinTractLength(min_tract_length); tracker->Update(); if (ext == ".fib" || ext == ".trk") { vtkSmartPointer< vtkPolyData > poly = tracker->GetFiberPolyData(); mitk::FiberBundle::Pointer outFib = mitk::FiberBundle::New(poly); if (compress > 0) outFib->Compress(compress); mitk::IOUtil::Save(outFib, outFile); } else { TrackerType::ItkDoubleImgType::Pointer outImg = tracker->GetOutputProbabilityMap(); mitk::Image::Pointer img = mitk::Image::New(); img->InitializeByItk(outImg.GetPointer()); img->SetVolume(outImg->GetBufferPointer()); if (ext != ".nii" && ext != ".nii.gz" && ext != ".nrrd") outFile += ".nii.gz"; mitk::IOUtil::Save(img, outFile); } delete handler; return EXIT_SUCCESS; } diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkStreamlineTrackingViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkStreamlineTrackingViewUserManual.dox index 7e619f3e6f..677c6f915b 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkStreamlineTrackingViewUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkStreamlineTrackingViewUserManual.dox @@ -1,95 +1,96 @@ /** \page org_mitk_views_streamlinetracking Streamline Tractography This view enables streamline tractography on various input data. The corresponding command line application is named "MitkStreamlineTractography". Available sections: - \ref StrTrackUserManualInputData - \ref StrTrackUserManualSeeding - \ref StrTrackUserManualConstraints - \ref StrTrackUserManualParameters - \ref StrTrackUserManualNeighbourhoodSampling - \ref StrTrackUserManualDataHandling - \ref StrTrackUserManualPostprocessing - \ref StrTrackUserManualReferences \section StrTrackUserManualInputData Input Data Select the data you want to track on in the datamanager. Supported file types are: - One or multiple DTI images selected in the datamanager. - One ODF image, e.g. obtained using MITK Q-ball reconstruction or MRtrix CSD (tractography similar to [6]). - One peak image (4D float image). - One raw diffusion-weighted image for machine learning based tractography [1]. -- Tractography Forest: Needed for machine learning based tractography [1]. \section StrTrackUserManualSeeding Seeding Specify how, where and how many tractography seed points are placed. This can be either done statically using a seed image or in an interactive fashion. Interactive tractography enables the dynamic placement of spherical seed regions simply by clicking into the image (similar to [5]). Image based seeding: - Seed Image: ROI image used to define the seed voxels. If no seed mask is specified, the whole image volume is seeded. - Seeds per voxel: If set to 1, the seed is defined as the voxel center. If > 1 the seeds are distributet randomly inside the voxel. Interactive seeding: - Update on Parameter Change: When "Update on Parameter Change" is checked, each parameter change causes an instant retracking with the new parameters. This enables an intuitive exploration of the effects that the other tractography parameters have on the resulting tractogram. - Radius: Radius of the manually placed spherical seed region. - Num.Seeds: Number of seeds placed randomly inside the spherical seed region. Parameters for both seeding modes: - Trials Per Seed: Try each seed N times until a valid streamline is obtained (only for probabilistic tractography). - Max. Num. Fibers: Tractography is stopped after the desired number of fibers is reached, even before all seed points are processed. \section StrTrackUserManualConstraints ROI Constraints Specify various ROI and mask images to constrain the tractography process. - Mask Image: ROI image used to constrain the generated streamlines, typically a brain mask. Streamlines that leave the regions defined in this image will stop immediately. -- Stop Image: ROI image used to define stopping regions. Streamlines that enter the regions defined in this image will stop immediately. +- Stop ROI Image: ROI image used to define stopping regions. Streamlines that enter the regions defined in this image will stop immediately. +- Exclusion ROI Image: Fibers that enter a region defined in this image will be discarded. - Endpoint Constraints: Determines which fibers are accepted based on their endpoint location. Options are: - No constraints on endpoint locations (command line option NONE) - Both EPs are required to be located in the target image (command line option EPS_IN_TARGET) - Both EPs are required to be located in the target image and the image values at the respective position needs to be distinct (command line option EPS_IN_TARGET_LABELDIFF) - One EP is required to be located in the seed image and one in the target image (command line option EPS_IN_SEED_AND_TARGET) - At least one EP is required to be located in the target image (command line option MIN_ONE_EP_IN_TARGET) - Exactly one EP is required to be located in the target image (command line option ONE_EP_IN_TARGET) - No EP is allowed to be located in the target image (command line option NO_EP_IN_TARGET) - Target Image: ROI image needed for endpoint constraints. \section StrTrackUserManualParameters Tractography Parameters - Mode: Toggle between deterministic and probabilistic tractography. Peak tracking only supports deterministic mode. The probabilistic method simply samples the output direction from the discrete probability ditribution provided by the discretized ODF. - Sharpen ODFs: If you are using dODF images as input, it is advisable to sharpen the ODFs (min-max normalize and raise to the power of 4). This is not necessary (and not recommended) for CSD fODFs, since they are naturally much sharper. - Cutoff: If the streamline reaches a position with an FA value or peak magnitude lower than the speciefied threshold, tracking is terminated. Typical values are 0.2 for FA/GFA and 0.1 for CSD peaks. - FA/GFA image used to determine streamline termination. If no image is specified, the FA/GFA image is automatically calculated from the input image. If multiple tensor images are used as input, it is recommended to provide such an image since the FA maps calculated from the individual input tensor images can not provide a suitable termination criterion. - ODF Cutoff: Additional threshold on the ODF magnitude. This is useful in case of CSD fODF tractography. For fODFs a good default value is 0.1, for normalized dODFs, e.g. Q-ball ODFs, this threshold should be very low (0.00025) or 0. - Step Size: The algorithm proceeds along the streamline with a fixed stepsize. Default is 0.5*minSpacing. - Min. Tract Length: Shorter fibers are discarded. - Angular threshold: Maximum angle between two successive steps (in degree). Default is 90° * step_size. For probabilistic tractography, candidate directions exceeding this threshold have probability 0, i.e. the respective ODF value is set to zero. The probabilities of the valid directions are normalized to sum to 1. - Loop Check: Stop streamline if the threshold on the angular stdev over the last 4 voxel lengths is exceeded. -1 = no loop check. - f and g values to balance between FACT [2] and TEND [3,4] tracking (only for tensor based tractography). For further information please refer to [2,3] \section StrTrackUserManualDataHandling Data Handling - Flip directions: Internally flips progression directions. This might be necessary depending on the input data. - Interpolate Tractography Data: Trilinearly interpolate the input image used for tractography. - Interpolate ROI Images: Trilinearly interpolate the ROI images used to constrain the tractography. \section StrTrackUserManualNeighbourhoodSampling Neighbourhood Sampling (for details see [1]) - Neighborhood Samples: Number of neighborhood samples that are used to determine the next fiber progression direction. - Sampling Distance: Distance of the sampling positions from the current streamline position (in voxels). - Use Only Frontal Samples: Only neighborhood samples in front of the current streamline position are considered. - Use Stop-Votes: If checked, the majority of sampling points has to place a stop-vote for the streamline to terminate. If not checked, all sampling positions have to vote for a streamline termination. \section StrTrackUserManualPostprocessing Output and Postprocessing - Compress Fibers: Whole brain tractograms obtained with a small step size can contain billions of points. The tractograms can be compressed by removing points that do not really contribute to the fiber shape, such as many points on a straight line. An error threshold (in mm) can be defined to specify which points should be removed and which not. - Output Probability Map: No streamline are generated. Instead, the tractography outputs a visitation-count map that indicates the probability of a fiber to reach a voxel from the selected seed region. For this measure to be sensible, the number of seeds per voxel needs to be rather large. \section StrTrackUserManualReferences References [1] Neher, Peter F., Marc-Alexandre Côté, Jean-Christophe Houde, Maxime Descoteaux, and Klaus H. Maier-Hein. “Fiber Tractography Using Machine Learning.” NeuroImage. Accessed July 19, 2017. doi:10.1016/j.neuroimage.2017.07.028.\n [2] Mori, Susumu, Walter E. Kaufmann, Godfrey D. Pearlson, Barbara J. Crain, Bram Stieltjes, Meiyappan Solaiyappan, and Peter C. M. Van Zijl. “In Vivo Visualization of Human Neural Pathways by Magnetic Resonance Imaging.” Annals of Neurology 47 (2000): 412–414.\n [3] Weinstein, David, Gordon Kindlmann, and Eric Lundberg. “Tensorlines: Advection-Diffusion Based Propagation through Diffusion Tensor Fields.” In Proceedings of the Conference on Visualization’99: Celebrating Ten Years, 249–253, n.d.\n [4] Lazar, Mariana, David M. Weinstein, Jay S. Tsuruda, Khader M. Hasan, Konstantinos Arfanakis, M. Elizabeth Meyerand, Benham Badie, et al. “White Matter Tractography Using Diffusion Tensor Deflection.” Human Brain Mapping 18, no. 4 (2003): 306–321.\n [5] Chamberland, M., K. Whittingstall, D. Fortin, D. Mathieu, and M. Descoteaux. “Real-Time Multi-Peak Tractography for Instantaneous Connectivity Display.” Front Neuroinform 8 (2014): 59. doi:10.3389/fninf.2014.00059.\n [6] Tournier, J-Donald, Fernando Calamante, and Alan Connelly. “MRtrix: Diffusion Tractography in Crossing Fiber Regions.” International Journal of Imaging Systems and Technology 22, no. 1 (March 2012): 53–66. doi:10.1002/ima.22005. */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp index 77dddc91a1..64376688e7 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp @@ -1,910 +1,921 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ // Blueberry #include #include #include // Qmitk #include "QmitkStreamlineTrackingView.h" #include "QmitkStdMultiWidget.h" // Qt #include // MITK #include #include #include #include #include #include #include #include #include #include #include #include // VTK #include #include #include #include #include #include #include #include #include const std::string QmitkStreamlineTrackingView::VIEW_ID = "org.mitk.views.streamlinetracking"; const std::string id_DataManager = "org.mitk.views.datamanager"; using namespace berry; QmitkStreamlineTrackingWorker::QmitkStreamlineTrackingWorker(QmitkStreamlineTrackingView* view) : m_View(view) { } void QmitkStreamlineTrackingWorker::run() { m_View->m_Tracker->Update(); m_View->m_TrackingThread.quit(); } QmitkStreamlineTrackingView::QmitkStreamlineTrackingView() : m_TrackingWorker(this) , m_Controls(nullptr) , m_FirstTensorProbRun(true) , m_FirstInteractiveRun(true) , m_TrackingHandler(nullptr) , m_ThreadIsRunning(false) , m_DeleteTrackingHandler(false) { m_TrackingWorker.moveToThread(&m_TrackingThread); connect(&m_TrackingThread, SIGNAL(started()), this, SLOT(BeforeThread())); connect(&m_TrackingThread, SIGNAL(started()), &m_TrackingWorker, SLOT(run())); connect(&m_TrackingThread, SIGNAL(finished()), this, SLOT(AfterThread())); m_TrackingTimer = new QTimer(this); } // Destructor QmitkStreamlineTrackingView::~QmitkStreamlineTrackingView() { if (m_Tracker.IsNull()) return; m_Tracker->SetStopTracking(true); m_TrackingThread.wait(); } void QmitkStreamlineTrackingView::CreateQtPartControl( QWidget *parent ) { if ( !m_Controls ) { // create GUI widgets from the Qt Designer's .ui file m_Controls = new Ui::QmitkStreamlineTrackingViewControls; m_Controls->setupUi( parent ); m_Controls->m_FaImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_SeedImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_MaskImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_TargetImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_StopImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_ForestBox->SetDataStorage(this->GetDataStorage()); + m_Controls->m_ExclusionImageBox->SetDataStorage(this->GetDataStorage()); mitk::TNodePredicateDataType::Pointer isImagePredicate = mitk::TNodePredicateDataType::New(); mitk::TNodePredicateDataType::Pointer isTractographyForest = mitk::TNodePredicateDataType::New(); mitk::NodePredicateProperty::Pointer isBinaryPredicate = mitk::NodePredicateProperty::New("binary", mitk::BoolProperty::New(true)); mitk::NodePredicateNot::Pointer isNotBinaryPredicate = mitk::NodePredicateNot::New( isBinaryPredicate ); mitk::NodePredicateAnd::Pointer isNotABinaryImagePredicate = mitk::NodePredicateAnd::New( isImagePredicate, isNotBinaryPredicate ); mitk::NodePredicateDimension::Pointer dimensionPredicate = mitk::NodePredicateDimension::New(3); m_Controls->m_ForestBox->SetPredicate(isTractographyForest); m_Controls->m_FaImageBox->SetPredicate( mitk::NodePredicateAnd::New(isNotABinaryImagePredicate, dimensionPredicate) ); m_Controls->m_FaImageBox->SetZeroEntryText("--"); m_Controls->m_SeedImageBox->SetPredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_SeedImageBox->SetZeroEntryText("--"); m_Controls->m_MaskImageBox->SetPredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_MaskImageBox->SetZeroEntryText("--"); m_Controls->m_StopImageBox->SetPredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_StopImageBox->SetZeroEntryText("--"); m_Controls->m_TargetImageBox->SetPredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_TargetImageBox->SetZeroEntryText("--"); + m_Controls->m_ExclusionImageBox->SetPredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); + m_Controls->m_ExclusionImageBox->SetZeroEntryText("--"); connect( m_TrackingTimer, SIGNAL(timeout()), this, SLOT(TimerUpdate()) ); connect( m_Controls->commandLinkButton_2, SIGNAL(clicked()), this, SLOT(StopTractography()) ); connect( m_Controls->commandLinkButton, SIGNAL(clicked()), this, SLOT(DoFiberTracking()) ); connect( m_Controls->m_InteractiveBox, SIGNAL(stateChanged(int)), this, SLOT(ToggleInteractive()) ); connect( m_Controls->m_ModeBox, SIGNAL(currentIndexChanged(int)), this, SLOT(UpdateGui()) ); connect( m_Controls->m_FaImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(DeleteTrackingHandler()) ); connect( m_Controls->m_ModeBox, SIGNAL(currentIndexChanged(int)), this, SLOT(DeleteTrackingHandler()) ); connect( m_Controls->m_OutputProbMap, SIGNAL(stateChanged(int)), this, SLOT(OutputStyleSwitched()) ); connect( m_Controls->m_SeedImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_ModeBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_StopImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_TargetImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); + connect( m_Controls->m_ExclusionImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_MaskImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FaImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_ForestBox, SIGNAL(currentIndexChanged(int)), this, SLOT(ForestSwitched()) ); connect( m_Controls->m_ForestBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SeedsPerVoxelBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_NumFibersBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_ScalarThresholdBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_OdfCutoffBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_StepSizeBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SamplingDistanceBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_AngularThresholdBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_MinTractLengthBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_fBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_gBox, SIGNAL(valueChanged(double)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_NumSamplesBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SeedRadiusBox, SIGNAL(valueChanged(double)), this, SLOT(InteractiveSeedChanged()) ); connect( m_Controls->m_NumSeedsBox, SIGNAL(valueChanged(int)), this, SLOT(InteractiveSeedChanged()) ); connect( m_Controls->m_OutputProbMap, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SharpenOdfsBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_InterpolationBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_MaskInterpolationBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FlipXBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FlipYBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FlipZBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FrontalSamplesBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_StopVotesBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_LoopCheckBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_TrialsPerSeedBox, SIGNAL(valueChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_EpConstraintsBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); StartStopTrackingGui(false); } UpdateGui(); } void QmitkStreamlineTrackingView::StopTractography() { if (m_Tracker.IsNull()) return; m_Tracker->SetStopTracking(true); } void QmitkStreamlineTrackingView::TimerUpdate() { if (m_Tracker.IsNull()) return; QString status_text(m_Tracker->GetStatusText().c_str()); m_Controls->m_StatusTextBox->setText(status_text); } void QmitkStreamlineTrackingView::BeforeThread() { m_TrackingTimer->start(1000); } void QmitkStreamlineTrackingView::AfterThread() { m_TrackingTimer->stop(); if (!m_Tracker->GetUseOutputProbabilityMap()) { vtkSmartPointer fiberBundle = m_Tracker->GetFiberPolyData(); if (!m_Controls->m_InteractiveBox->isChecked() && fiberBundle->GetNumberOfLines() == 0) { QMessageBox warnBox; warnBox.setWindowTitle("Warning"); warnBox.setText("No fiberbundle was generated!"); warnBox.setDetailedText("No fibers were generated using the chosen parameters. Typical reasons are:\n\n- Cutoff too high. Some images feature very low FA/GFA/peak size. Try to lower this parameter.\n- Angular threshold too strict. Try to increase this parameter.\n- A small step sizes also means many steps to go wrong. Especially in the case of probabilistic tractography. Try to adjust the angular threshold."); warnBox.setIcon(QMessageBox::Warning); warnBox.exec(); if (m_InteractivePointSetNode.IsNotNull()) m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,1,1)); StartStopTrackingGui(false); if (m_DeleteTrackingHandler) DeleteTrackingHandler(); UpdateGui(); return; } mitk::FiberBundle::Pointer fib = mitk::FiberBundle::New(fiberBundle); fib->SetReferenceGeometry(dynamic_cast(m_ParentNode->GetData())->GetGeometry()); if (m_Controls->m_ResampleFibersBox->isChecked() && fiberBundle->GetNumberOfLines()>0) fib->Compress(m_Controls->m_FiberErrorBox->value()); fib->ColorFibersByOrientation(); m_Tracker->SetDicomProperties(fib); if (m_Controls->m_InteractiveBox->isChecked()) { if (m_InteractiveNode.IsNull()) { m_InteractiveNode = mitk::DataNode::New(); QString name("Interactive"); m_InteractiveNode->SetName(name.toStdString()); GetDataStorage()->Add(m_InteractiveNode); } m_InteractiveNode->SetData(fib); m_InteractiveNode->SetFloatProperty("Fiber2DSliceThickness", m_Tracker->GetMinVoxelSize()/2); if (auto renderWindowPart = this->GetRenderWindowPart()) renderWindowPart->RequestUpdate(); } else { mitk::DataNode::Pointer node = mitk::DataNode::New(); node->SetData(fib); QString name("FiberBundle_"); name += m_ParentNode->GetName().c_str(); name += "_Streamline"; node->SetName(name.toStdString()); node->SetFloatProperty("Fiber2DSliceThickness", m_Tracker->GetMinVoxelSize()/2); GetDataStorage()->Add(node, m_ParentNode); } } else { TrackerType::ItkDoubleImgType::Pointer outImg = m_Tracker->GetOutputProbabilityMap(); mitk::Image::Pointer img = mitk::Image::New(); img->InitializeByItk(outImg.GetPointer()); img->SetVolume(outImg->GetBufferPointer()); if (m_Controls->m_InteractiveBox->isChecked()) { if (m_InteractiveNode.IsNull()) { m_InteractiveNode = mitk::DataNode::New(); QString name("Interactive"); m_InteractiveNode->SetName(name.toStdString()); GetDataStorage()->Add(m_InteractiveNode); } m_InteractiveNode->SetData(img); mitk::LookupTable::Pointer lut = mitk::LookupTable::New(); lut->SetType(mitk::LookupTable::JET_TRANSPARENT); mitk::LookupTableProperty::Pointer lut_prop = mitk::LookupTableProperty::New(); lut_prop->SetLookupTable(lut); m_InteractiveNode->SetProperty("LookupTable", lut_prop); m_InteractiveNode->SetProperty("opacity", mitk::FloatProperty::New(0.5)); m_InteractiveNode->SetFloatProperty("Fiber2DSliceThickness", m_Tracker->GetMinVoxelSize()/2); if (auto renderWindowPart = this->GetRenderWindowPart()) renderWindowPart->RequestUpdate(); } else { mitk::DataNode::Pointer node = mitk::DataNode::New(); node->SetData(img); QString name("ProbabilityMap_"); name += m_ParentNode->GetName().c_str(); node->SetName(name.toStdString()); mitk::LookupTable::Pointer lut = mitk::LookupTable::New(); lut->SetType(mitk::LookupTable::JET_TRANSPARENT); mitk::LookupTableProperty::Pointer lut_prop = mitk::LookupTableProperty::New(); lut_prop->SetLookupTable(lut); node->SetProperty("LookupTable", lut_prop); node->SetProperty("opacity", mitk::FloatProperty::New(0.5)); GetDataStorage()->Add(node, m_ParentNode); } } if (m_InteractivePointSetNode.IsNotNull()) m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,1,1)); StartStopTrackingGui(false); if (m_DeleteTrackingHandler) DeleteTrackingHandler(); UpdateGui(); } void QmitkStreamlineTrackingView::InteractiveSeedChanged(bool posChanged) { if (m_ThreadIsRunning) return; if (!posChanged && (!m_Controls->m_InteractiveBox->isChecked() || !m_Controls->m_ParamUpdateBox->isChecked())) return; std::srand(std::time(0)); m_SeedPoints.clear(); itk::Point world_pos = this->GetRenderWindowPart()->GetSelectedPosition(); m_SeedPoints.push_back(world_pos); float radius = m_Controls->m_SeedRadiusBox->value(); int num = m_Controls->m_NumSeedsBox->value(); mitk::PointSet::Pointer pointset = mitk::PointSet::New(); pointset->InsertPoint(0, world_pos); m_InteractivePointSetNode->SetProperty("pointsize", mitk::FloatProperty::New(radius*2)); m_InteractivePointSetNode->SetProperty("point 2D size", mitk::FloatProperty::New(radius*2)); m_InteractivePointSetNode->SetData(pointset); for (int i=1; i p; p[0] = rand()%1000-500; p[1] = rand()%1000-500; p[2] = rand()%1000-500; p.Normalize(); p *= radius; m_SeedPoints.push_back(world_pos+p); } m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,0,0)); DoFiberTracking(); } void QmitkStreamlineTrackingView::OnParameterChanged() { UpdateGui(); if (m_Controls->m_InteractiveBox->isChecked() && m_Controls->m_ParamUpdateBox->isChecked()) DoFiberTracking(); } void QmitkStreamlineTrackingView::ToggleInteractive() { UpdateGui(); m_Controls->m_SeedsPerVoxelBox->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); m_Controls->m_SeedsPerVoxelLabel->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); m_Controls->m_SeedImageBox->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); m_Controls->label_6->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); if ( m_Controls->m_InteractiveBox->isChecked() ) { // if (m_FirstInteractiveRun) // { // QMessageBox::information(nullptr, "Information", "Place and move a spherical seed region anywhere in the image by left-clicking and dragging. If the seed region is colored red, tracking is in progress. If the seed region is colored white, tracking is finished.\nPlacing the seed region for the first time in a newly selected dataset might cause a short delay, since the tracker needs to be initialized."); // m_FirstInteractiveRun = false; // } QApplication::setOverrideCursor(Qt::PointingHandCursor); QApplication::processEvents(); m_InteractivePointSetNode = mitk::DataNode::New(); m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,1,1)); m_InteractivePointSetNode->SetName("InteractiveSeedRegion"); mitk::PointSetShapeProperty::Pointer shape_prop = mitk::PointSetShapeProperty::New(); shape_prop->SetValue(mitk::PointSetShapeProperty::PointSetShape::CIRCLE); m_InteractivePointSetNode->SetProperty("Pointset.2D.shape", shape_prop); GetDataStorage()->Add(m_InteractivePointSetNode); m_SliceChangeListener.RenderWindowPartActivated(this->GetRenderWindowPart()); connect(&m_SliceChangeListener, SIGNAL(SliceChanged()), this, SLOT(OnSliceChanged())); } else { QApplication::restoreOverrideCursor(); QApplication::processEvents(); m_InteractiveNode = nullptr; m_InteractivePointSetNode = nullptr; m_SliceChangeListener.RenderWindowPartActivated(this->GetRenderWindowPart()); disconnect(&m_SliceChangeListener, SIGNAL(SliceChanged()), this, SLOT(OnSliceChanged())); } } void QmitkStreamlineTrackingView::OnSliceChanged() { InteractiveSeedChanged(true); } void QmitkStreamlineTrackingView::SetFocus() { } void QmitkStreamlineTrackingView::DeleteTrackingHandler() { if (!m_ThreadIsRunning && m_TrackingHandler != nullptr) { delete m_TrackingHandler; m_TrackingHandler = nullptr; m_DeleteTrackingHandler = false; } else if (m_ThreadIsRunning) { m_DeleteTrackingHandler = true; } } void QmitkStreamlineTrackingView::ForestSwitched() { DeleteTrackingHandler(); } void QmitkStreamlineTrackingView::OutputStyleSwitched() { if (m_InteractiveNode.IsNotNull()) GetDataStorage()->Remove(m_InteractiveNode); m_InteractiveNode = nullptr; } void QmitkStreamlineTrackingView::OnSelectionChanged( berry::IWorkbenchPart::Pointer , const QList& nodes ) { std::vector< mitk::DataNode::Pointer > last_nodes = m_InputImageNodes; m_InputImageNodes.clear(); m_InputImages.clear(); m_AdditionalInputImages.clear(); bool retrack = false; for( auto node : nodes ) { if( node.IsNotNull() && dynamic_cast(node->GetData()) ) { if( dynamic_cast(node->GetData()) ) { m_InputImageNodes.push_back(node); m_InputImages.push_back(dynamic_cast(node->GetData())); retrack = true; } else if ( dynamic_cast(node->GetData()) ) { m_InputImageNodes.push_back(node); m_InputImages.push_back(dynamic_cast(node->GetData())); retrack = true; } else if ( mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage( dynamic_cast(node->GetData())) ) { m_InputImageNodes.push_back(node); m_InputImages.push_back(dynamic_cast(node->GetData())); retrack = true; } else { mitk::Image* img = dynamic_cast(node->GetData()); if (img!=nullptr) { int dim = img->GetDimension(); unsigned int* dimensions = img->GetDimensions(); if (dim==4 && dimensions[3]%3==0) { m_InputImageNodes.push_back(node); m_InputImages.push_back(dynamic_cast(node->GetData())); retrack = true; } else if (dim==3) { m_AdditionalInputImages.push_back(dynamic_cast(node->GetData())); } } } } } // sometimes the OnSelectionChanged event is sent twice and actually no selection has changed for the first event. We need to catch that. if (last_nodes.size() == m_InputImageNodes.size()) { bool same_nodes = true; for (unsigned int i=0; im_TensorImageLabel->setText("select in data-manager"); m_Controls->m_fBox->setEnabled(false); m_Controls->m_fLabel->setEnabled(false); m_Controls->m_gBox->setEnabled(false); m_Controls->m_gLabel->setEnabled(false); m_Controls->m_FaImageBox->setEnabled(true); m_Controls->mFaImageLabel->setEnabled(true); m_Controls->m_OdfCutoffBox->setEnabled(false); m_Controls->m_OdfCutoffLabel->setEnabled(false); m_Controls->m_SharpenOdfsBox->setEnabled(false); m_Controls->m_ForestBox->setVisible(false); m_Controls->m_ForestLabel->setVisible(false); m_Controls->commandLinkButton->setEnabled(false); m_Controls->m_TrialsPerSeedBox->setEnabled(false); m_Controls->m_TrialsPerSeedLabel->setEnabled(false); m_Controls->m_TargetImageBox->setVisible(false); m_Controls->m_TargetImageLabel->setVisible(false); if (m_Controls->m_InteractiveBox->isChecked()) { m_Controls->m_InteractiveSeedingFrame->setVisible(true); m_Controls->m_StaticSeedingFrame->setVisible(false); m_Controls->commandLinkButton_2->setVisible(false); m_Controls->commandLinkButton->setVisible(false); } else { m_Controls->m_InteractiveSeedingFrame->setVisible(false); m_Controls->m_StaticSeedingFrame->setVisible(true); m_Controls->commandLinkButton_2->setVisible(m_ThreadIsRunning); m_Controls->commandLinkButton->setVisible(!m_ThreadIsRunning); } if (m_Controls->m_EpConstraintsBox->currentIndex()>0) { m_Controls->m_TargetImageBox->setVisible(true); m_Controls->m_TargetImageLabel->setVisible(true); } // trials per seed are only important for probabilistic tractography if (m_Controls->m_ModeBox->currentIndex()==1) { m_Controls->m_TrialsPerSeedBox->setEnabled(true); m_Controls->m_TrialsPerSeedLabel->setEnabled(true); } if(!m_InputImageNodes.empty()) { if (m_InputImageNodes.size()>1) m_Controls->m_TensorImageLabel->setText( ( std::to_string(m_InputImageNodes.size()) + " images selected").c_str() ); else m_Controls->m_TensorImageLabel->setText(m_InputImageNodes.at(0)->GetName().c_str()); m_Controls->commandLinkButton->setEnabled(!m_Controls->m_InteractiveBox->isChecked() && !m_ThreadIsRunning); m_Controls->m_ScalarThresholdBox->setEnabled(true); m_Controls->m_FaThresholdLabel->setEnabled(true); if ( dynamic_cast(m_InputImageNodes.at(0)->GetData()) ) { m_Controls->m_fBox->setEnabled(true); m_Controls->m_fLabel->setEnabled(true); m_Controls->m_gBox->setEnabled(true); m_Controls->m_gLabel->setEnabled(true); } else if ( dynamic_cast(m_InputImageNodes.at(0)->GetData()) ) { m_Controls->m_OdfCutoffBox->setEnabled(true); m_Controls->m_OdfCutoffLabel->setEnabled(true); m_Controls->m_SharpenOdfsBox->setEnabled(true); } else if ( mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage( dynamic_cast(m_InputImageNodes.at(0)->GetData())) ) { m_Controls->m_ForestBox->setVisible(true); m_Controls->m_ForestLabel->setVisible(true); m_Controls->m_ScalarThresholdBox->setEnabled(false); m_Controls->m_FaThresholdLabel->setEnabled(false); } } } void QmitkStreamlineTrackingView::StartStopTrackingGui(bool start) { m_ThreadIsRunning = start; if (!m_Controls->m_InteractiveBox->isChecked()) { m_Controls->commandLinkButton_2->setVisible(start); m_Controls->commandLinkButton->setVisible(!start); m_Controls->m_InteractiveBox->setEnabled(!start); m_Controls->m_StatusTextBox->setVisible(start); } } void QmitkStreamlineTrackingView::DoFiberTracking() { if (m_ThreadIsRunning) return; if (m_InputImages.empty()) return; if (m_Controls->m_InteractiveBox->isChecked() && m_SeedPoints.empty()) return; StartStopTrackingGui(true); m_Tracker = TrackerType::New(); if( dynamic_cast(m_InputImageNodes.at(0)->GetData()) ) { typedef mitk::ImageToItk CasterType; if (m_Controls->m_ModeBox->currentIndex()==1) { if (m_InputImages.size()>1) { QMessageBox::information(nullptr, "Information", "Probabilistic tensor tractography is only implemented for single-tensor mode!"); StartStopTrackingGui(false); return; } // if (m_FirstTensorProbRun) // { // QMessageBox::information(nullptr, "Information", "Internally calculating ODF from tensor image and performing probabilistic ODF tractography. ODFs are sharpened (min-max normalized and raised to the power of 4). TEND parameters are ignored."); // m_FirstTensorProbRun = false; // } if (m_TrackingHandler==nullptr) { typedef mitk::ImageToItk< mitk::TrackingHandlerOdf::ItkOdfImageType > CasterType; m_TrackingHandler = new mitk::TrackingHandlerOdf(); mitk::TensorImage::ItkTensorImageType::Pointer itkImg = mitk::TensorImage::ItkTensorImageType::New(); mitk::CastToItkImage(m_InputImages.at(0), itkImg); typedef itk::TensorImageToOdfImageFilter< float, float > FilterType; FilterType::Pointer filter = FilterType::New(); filter->SetInput( itkImg ); filter->Update(); dynamic_cast(m_TrackingHandler)->SetOdfImage(filter->GetOutput()); if (m_Controls->m_FaImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer itkImg = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_FaImageBox->GetSelectedNode()->GetData()), itkImg); dynamic_cast(m_TrackingHandler)->SetGfaImage(itkImg); } } dynamic_cast(m_TrackingHandler)->SetGfaThreshold(m_Controls->m_ScalarThresholdBox->value()); dynamic_cast(m_TrackingHandler)->SetOdfThreshold(0); dynamic_cast(m_TrackingHandler)->SetSharpenOdfs(true); dynamic_cast(m_TrackingHandler)->SetIsOdfFromTensor(true); } else { if (m_TrackingHandler==nullptr) { m_TrackingHandler = new mitk::TrackingHandlerTensor(); for (int i=0; i<(int)m_InputImages.size(); i++) { typedef mitk::ImageToItk< mitk::TrackingHandlerTensor::ItkTensorImageType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(m_InputImages.at(i)); caster->Update(); mitk::TrackingHandlerTensor::ItkTensorImageType::ConstPointer itkImg = caster->GetOutput(); dynamic_cast(m_TrackingHandler)->AddTensorImage(itkImg); } if (m_Controls->m_FaImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer itkImg = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_FaImageBox->GetSelectedNode()->GetData()), itkImg); dynamic_cast(m_TrackingHandler)->SetFaImage(itkImg); } } dynamic_cast(m_TrackingHandler)->SetFaThreshold(m_Controls->m_ScalarThresholdBox->value()); dynamic_cast(m_TrackingHandler)->SetF((float)m_Controls->m_fBox->value()); dynamic_cast(m_TrackingHandler)->SetG((float)m_Controls->m_gBox->value()); } } else if ( dynamic_cast(m_InputImageNodes.at(0)->GetData()) ) { if (m_TrackingHandler==nullptr) { typedef mitk::ImageToItk< mitk::TrackingHandlerOdf::ItkOdfImageType > CasterType; m_TrackingHandler = new mitk::TrackingHandlerOdf(); mitk::TrackingHandlerOdf::ItkOdfImageType::Pointer itkImg = mitk::TrackingHandlerOdf::ItkOdfImageType::New(); mitk::CastToItkImage(m_InputImages.at(0), itkImg); dynamic_cast(m_TrackingHandler)->SetOdfImage(itkImg); if (m_Controls->m_FaImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer itkImg = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_FaImageBox->GetSelectedNode()->GetData()), itkImg); dynamic_cast(m_TrackingHandler)->SetGfaImage(itkImg); } } dynamic_cast(m_TrackingHandler)->SetGfaThreshold(m_Controls->m_ScalarThresholdBox->value()); dynamic_cast(m_TrackingHandler)->SetOdfThreshold(m_Controls->m_OdfCutoffBox->value()); dynamic_cast(m_TrackingHandler)->SetSharpenOdfs(m_Controls->m_SharpenOdfsBox->isChecked()); } else if ( mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage( dynamic_cast(m_InputImageNodes.at(0)->GetData())) ) { if ( m_Controls->m_ForestBox->GetSelectedNode().IsNull() ) { QMessageBox::information(nullptr, "Information", "Not random forest for machine learning based tractography (raw dMRI tractography) selected. Did you accidentally select the raw diffusion-weighted image in the datamanager?"); StartStopTrackingGui(false); return; } if (m_TrackingHandler==nullptr) { mitk::TractographyForest::Pointer forest = dynamic_cast(m_Controls->m_ForestBox->GetSelectedNode()->GetData()); mitk::Image::Pointer dwi = dynamic_cast(m_InputImageNodes.at(0)->GetData()); std::vector< std::vector< ItkFloatImageType::Pointer > > additionalFeatureImages; additionalFeatureImages.push_back(std::vector< ItkFloatImageType::Pointer >()); for (auto img : m_AdditionalInputImages) { ItkFloatImageType::Pointer itkimg = ItkFloatImageType::New(); mitk::CastToItkImage(img, itkimg); additionalFeatureImages.at(0).push_back(itkimg); } bool forest_valid = false; if (forest->GetNumFeatures()>=100) { int num_previous_directions = (forest->GetNumFeatures() - (100 + additionalFeatureImages.at(0).size()))/3; m_TrackingHandler = new mitk::TrackingHandlerRandomForest<6, 100>(); dynamic_cast*>(m_TrackingHandler)->AddDwi(dwi); dynamic_cast*>(m_TrackingHandler)->SetAdditionalFeatureImages(additionalFeatureImages); dynamic_cast*>(m_TrackingHandler)->SetForest(forest); dynamic_cast*>(m_TrackingHandler)->SetNumPreviousDirections(num_previous_directions); forest_valid = dynamic_cast*>(m_TrackingHandler)->IsForestValid(); } else { int num_previous_directions = (forest->GetNumFeatures() - (28 + additionalFeatureImages.at(0).size()))/3; m_TrackingHandler = new mitk::TrackingHandlerRandomForest<6, 28>(); dynamic_cast*>(m_TrackingHandler)->AddDwi(dwi); dynamic_cast*>(m_TrackingHandler)->SetAdditionalFeatureImages(additionalFeatureImages); dynamic_cast*>(m_TrackingHandler)->SetForest(forest); dynamic_cast*>(m_TrackingHandler)->SetNumPreviousDirections(num_previous_directions); forest_valid = dynamic_cast*>(m_TrackingHandler)->IsForestValid(); } if (!forest_valid) { QMessageBox::information(nullptr, "Information", "Random forest is invalid. The forest signatue does not match the parameters of TrackingHandlerRandomForest."); StartStopTrackingGui(false); return; } } } else { if (m_Controls->m_ModeBox->currentIndex()==1) { QMessageBox::information(nullptr, "Information", "Probabilstic tractography is not implemented for peak images."); StartStopTrackingGui(false); return; } try { if (m_TrackingHandler==nullptr) { typedef mitk::ImageToItk< mitk::TrackingHandlerPeaks::PeakImgType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(m_InputImages.at(0)); caster->Update(); mitk::TrackingHandlerPeaks::PeakImgType::Pointer itkImg = caster->GetOutput(); m_TrackingHandler = new mitk::TrackingHandlerPeaks(); dynamic_cast(m_TrackingHandler)->SetPeakImage(itkImg); } dynamic_cast(m_TrackingHandler)->SetPeakThreshold(m_Controls->m_ScalarThresholdBox->value()); } catch(...) { QMessageBox::information(nullptr, "Error", "Peak tracker could not be initialized. Is your input image in the correct format (4D float image, peaks in the 4th dimension)?"); StartStopTrackingGui(false); return; } } m_TrackingHandler->SetFlipX(m_Controls->m_FlipXBox->isChecked()); m_TrackingHandler->SetFlipY(m_Controls->m_FlipYBox->isChecked()); m_TrackingHandler->SetFlipZ(m_Controls->m_FlipZBox->isChecked()); m_TrackingHandler->SetInterpolate(m_Controls->m_InterpolationBox->isChecked()); switch (m_Controls->m_ModeBox->currentIndex()) { case 0: m_TrackingHandler->SetMode(mitk::TrackingDataHandler::MODE::DETERMINISTIC); break; case 1: m_TrackingHandler->SetMode(mitk::TrackingDataHandler::MODE::PROBABILISTIC); break; default: m_TrackingHandler->SetMode(mitk::TrackingDataHandler::MODE::DETERMINISTIC); } if (m_Controls->m_InteractiveBox->isChecked()) { m_Tracker->SetSeedPoints(m_SeedPoints); } else if (m_Controls->m_SeedImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_SeedImageBox->GetSelectedNode()->GetData()), mask); m_Tracker->SetSeedImage(mask); } if (m_Controls->m_MaskImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_MaskImageBox->GetSelectedNode()->GetData()), mask); m_Tracker->SetMaskImage(mask); } if (m_Controls->m_StopImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_StopImageBox->GetSelectedNode()->GetData()), mask); m_Tracker->SetStoppingRegions(mask); } if (m_Controls->m_TargetImageBox->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_TargetImageBox->GetSelectedNode()->GetData()), mask); m_Tracker->SetTargetRegions(mask); } + if (m_Controls->m_ExclusionImageBox->GetSelectedNode().IsNotNull()) + { + ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); + mitk::CastToItkImage(dynamic_cast(m_Controls->m_ExclusionImageBox->GetSelectedNode()->GetData()), mask); + m_Tracker->SetExclusionRegions(mask); + } + // Endpoint constraints switch (m_Controls->m_EpConstraintsBox->currentIndex()) { case 0: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::NONE); m_Tracker->SetTargetRegions(nullptr); break; case 1: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET); break; case 2: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET_LABELDIFF); break; case 3: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_SEED_AND_TARGET); break; case 4: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::MIN_ONE_EP_IN_TARGET); break; case 5: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::ONE_EP_IN_TARGET); break; case 6: m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::NO_EP_IN_TARGET); break; } if (m_Tracker->GetEndpointConstraint()!=itk::StreamlineTrackingFilter::EndpointConstraints::NONE && m_Controls->m_TargetImageBox->GetSelectedNode().IsNull()) { QMessageBox::information(nullptr, "Error", "Endpoint constraints are used but no target image is set!"); StartStopTrackingGui(false); return; } else if (m_Tracker->GetEndpointConstraint()==itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_SEED_AND_TARGET && (m_Controls->m_SeedImageBox->GetSelectedNode().IsNull()|| m_Controls->m_TargetImageBox->GetSelectedNode().IsNull()) ) { QMessageBox::information(nullptr, "Error", "Endpoint constraint EPS_IN_SEED_AND_TARGET is used but no target or no seed image is set!"); StartStopTrackingGui(false); return; } m_Tracker->SetInterpolateMasks(m_Controls->m_MaskInterpolationBox->isChecked()); m_Tracker->SetVerbose(!m_Controls->m_InteractiveBox->isChecked()); m_Tracker->SetSeedsPerVoxel(m_Controls->m_SeedsPerVoxelBox->value()); m_Tracker->SetStepSize(m_Controls->m_StepSizeBox->value()); m_Tracker->SetSamplingDistance(m_Controls->m_SamplingDistanceBox->value()); m_Tracker->SetUseStopVotes(m_Controls->m_StopVotesBox->isChecked()); m_Tracker->SetOnlyForwardSamples(m_Controls->m_FrontalSamplesBox->isChecked()); m_Tracker->SetTrialsPerSeed(m_Controls->m_TrialsPerSeedBox->value()); m_Tracker->SetMaxNumTracts(m_Controls->m_NumFibersBox->value()); m_Tracker->SetNumberOfSamples(m_Controls->m_NumSamplesBox->value()); m_Tracker->SetTrackingHandler(m_TrackingHandler); m_Tracker->SetLoopCheck(m_Controls->m_LoopCheckBox->value()); m_Tracker->SetAngularThreshold(m_Controls->m_AngularThresholdBox->value()); m_Tracker->SetMinTractLength(m_Controls->m_MinTractLengthBox->value()); m_Tracker->SetUseOutputProbabilityMap(m_Controls->m_OutputProbMap->isChecked()); m_ParentNode = m_InputImageNodes.at(0); m_TrackingThread.start(QThread::LowestPriority); } diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingViewControls.ui b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingViewControls.ui index b608549608..0d11d794d1 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingViewControls.ui +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingViewControls.ui @@ -1,1398 +1,1455 @@ QmitkStreamlineTrackingViewControls 0 0 453 859 0 0 QmitkTemplate 3 3 0 40 QFrame::NoFrame QFrame::Raised 0 15 0 0 6 15 true 0 0 true QFrame::NoFrame QFrame::Raised 0 0 0 0 Input Image. ODF, tensor and peak images are currently supported. Input Image: Input Image. ODF, tensor, peak, and, in case of ML tractography, raw diffusion-weighted images are currently supported. <html><head/><body><p><span style=" color:#ff0000;">select image in data-manager</span></p></body></html> true Tractography Forest: Random forest for machine learning based tractography. QComboBox::AdjustToMinimumContentsLength - true Stop tractography and return all fibers reconstructed until now. Stop Tractography false Start Tractography 0 0 0 0 0 0 0 - 435 - 297 + 421 + 267 Seeding Specify how, where and how many tractography seed points are placed. QFrame::NoFrame QFrame::Raised 0 0 0 0 QFrame::NoFrame QFrame::Raised 0 0 0 0 Number of seed points equally distributed around selected position. 1 9999999 50 Radius: Seedpoints are equally distributed within a sphere centered at the selected position with the specified radius (in mm). 2 50.000000000000000 0.100000000000000 2.000000000000000 Num. Seeds: true When checked, parameter changes cause instant retracking while in interactive mode. Update on Parameter Change true QFrame::NoFrame QFrame::Raised 0 0 0 0 Try each seed N times until a valid streamline is obtained (only for probabilistic tractography). Minimum fiber length (in mm) 1 999 10 Trials Per Seed: Max. Num. Fibers: Tractography is stopped after the desired number of fibers is reached, even before all seed points are processed (-1 means no limit). -1 999999999 -1 QFrame::NoFrame QFrame::Raised 0 0 0 0 Number of seed points placed in each voxel. 1 9999999 Seeds per Voxel: Seed points are only placed inside the regions defined in the seed image. If no seed image is selected, the whole image is seeded. QComboBox::AdjustToMinimumContentsLength - Seed Image: true Dynamically pick a seed location by click into image. - Enable Interactive Tractography + Enable Interactive Tractography Qt::Vertical 20 40 0 0 435 - 297 + 255 ROI Constraints Specify various ROI and mask images to constrain the tractography process. - + - Target Image: + Target ROI Image: - + + + + + + + Mask Image: + + + + Select which fibers should be accepted or rejected based on the location of their endpoints. QComboBox::AdjustToMinimumContentsLength No Constraints on EP locations Both EPs in Target Image Both EPs in Target Image But Different Label One EP in Seed Image and One EP in Target Image At Least One EP in Target Image Exactly One EP in Target Image No EP in Target Image - - - - - - - Stop Image: - - - - - - - - - - Mask Image: - - - - + The target image is used for the endpoint constraint strategy defined above. QComboBox::AdjustToMinimumContentsLength - - + + + + + + + Stop ROI Image: + + + + Endpoint Constraints: Fibers that leave the regions defined in this image will stop immediately. QComboBox::AdjustToMinimumContentsLength - Fibers that enter a region defined in this image will stop immediately. QComboBox::AdjustToMinimumContentsLength - - + Qt::Vertical 20 40 + + + + + + + Exclusion ROI Image: + + + + + + + Fibers that enter a region defined in this image will be discarded. + + + QComboBox::AdjustToMinimumContentsLength + + + + - + + + + + + + 0 + 0 + 421 + 359 + + Tractography Parameters Specify the behavior of the tractography at each streamline integration step (step size, deterministic/probabilistic, ...). Qt::Vertical 20 40 f=1 + g=0 means FACT (depending on the chosen interpolation). f=0 and g=1 means TEND (disable interpolation for this mode!). 2 1.000000000000000 0.100000000000000 0.000000000000000 Toggle between deterministic and probabilistic tractography. Some modes might not be available for all types of tractography. Deterministic Probabilistic Cutoff: FA/GFA Image: Mode: Angular Threshold: Step size (in voxels) 2 0.010000000000000 10.000000000000000 0.100000000000000 0.500000000000000 Maximum allowed angular SDTEV over 4 voxel lengths. Default: no loop check. -1 180 -1 If an image is selected, the stopping criterion is not calculated from the input image but instead the selected image is used. QComboBox::AdjustToMinimumContentsLength - Step Size: Additional threshold on the ODF magnitude. This is useful in case of CSD fODF tractography. For fODFs a good default value is 0.1, for normalized dODFs, e.g. Q-ball ODFs, this threshold should be very low (0.00025) or 0. 5 1.000000000000000 0.100000000000000 0.000250000000000 f=1 + g=0 means FACT (depending on the chosen interpolation). f=0 and g=1 means TEND (disable interpolation for this mode!). 2 1.000000000000000 0.100000000000000 1.000000000000000 If you are using dODF images as input, it is advisable to sharpen the ODFs (min-max normalize and raise to the power of 4). This is not necessary for CSD fODFs, since they are naturally much sharper. f parameter of tensor tractography. f=1 + g=0 means FACT (depending on the chosen interpolation). f=0 and g=1 means TEND (disable interpolation for this mode!). f: Min. Tract Length: Threshold on peak magnitude, FA, GFA, ... 5 1.000000000000000 0.100000000000000 0.100000000000000 ODF Cutoff: Minimum tract length in mm. Shorter fibers are discarded. Minimum fiber length (in mm) 1 999.000000000000000 1.000000000000000 20.000000000000000 Loop Check: Angular threshold between two steps (in degree). Default: 90° * step_size -1 90 1 -1 g: Sharpen ODFs: + + + 0 + 0 + 435 + 255 + + Neighborhood Sampling Specify if and how information about the current streamline neighborhood should be used. Only neighborhood samples in front of the current streamline position are considered. Use Only Frontal Samples false If checked, the majority of sampling points has to place a stop-vote for the streamline to terminate. If not checked, all sampling positions have to vote for a streamline termination. Use Stop-Votes false QFrame::NoFrame QFrame::Raised 0 0 0 0 Num. Samples: Number of neighborhood samples that are used to determine the next fiber progression direction. 50 Sampling Distance: Sampling distance (in voxels) 2 10.000000000000000 0.100000000000000 0.250000000000000 Qt::Vertical 20 40 + + + 0 + 0 + 435 + 255 + + Data Handling Specify interpolation and direction flips. QFrame::NoFrame QFrame::Raised 0 0 0 0 Trilinearly interpolate the input image used for tractography. Interpolate Tractography Data true Trilinearly interpolate the ROI images used to constrain the tractography. Interpolate ROI Images true QFrame::NoFrame QFrame::Raised 0 0 0 0 QFrame::NoFrame QFrame::Raised 0 0 0 0 Internally flips progression directions. This might be necessary depending on the input data. x Internally flips progression directions. This might be necessary depending on the input data. y Internally flips progression directions. This might be necessary depending on the input data. z Flip directions: Qt::Vertical 20 40 + + + 0 + 0 + 435 + 255 + + Output and Postprocessing Specify the tractography output (streamlines or probability maps) and postprocessing steps. QFrame::NoFrame QFrame::Raised 0 0 0 0 Compress fibers using the specified error constraint. Compress Fibers true Qt::StrongFocus Lossy fiber compression. Recommended for large tractograms. Maximum error in mm. 3 10.000000000000000 0.010000000000000 0.100000000000000 Output map with voxel-wise visitation counts instead of streamlines. Output Probability Map false Qt::Vertical 20 40 QmitkDataStorageComboBox QComboBox
QmitkDataStorageComboBox.h
 QmitkDataStorageComboBoxWithSelectNone QComboBox
QmitkDataStorageComboBoxWithSelectNone.h