diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.registration/documentation/UserManual/QmitkSimpleRegistration.dox b/Plugins/org.mitk.gui.qt.diffusionimaging.registration/documentation/UserManual/QmitkSimpleRegistration.dox index 50f5be7cdd..b6fc0bb470 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.registration/documentation/UserManual/QmitkSimpleRegistration.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.registration/documentation/UserManual/QmitkSimpleRegistration.dox @@ -1,8 +1,6 @@ /** \page org_mitk_views_simpleregistrationview Registration -This view enables the simple rigid or affine registration of two images. The registered image will be displayed in transparent blue color overlayed over the fixed image. To regain normal coloring, right-click on the data node and adjust the corresponding settings. - -It is also possible to transform a tractogram with a registration object obtained from a previous registration of two images. +This view enables the simple rigid or affine registration of two images. It is also possible to transform a tractogram with a registration object obtained from a previous registration of two images. */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.registration/src/internal/QmitkSimpleRegistrationView.cpp b/Plugins/org.mitk.gui.qt.diffusionimaging.registration/src/internal/QmitkSimpleRegistrationView.cpp index 7905469296..dfb58a2792 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.registration/src/internal/QmitkSimpleRegistrationView.cpp +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.registration/src/internal/QmitkSimpleRegistrationView.cpp @@ -1,392 +1,392 @@ /*=================================================================== 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. ===================================================================*/ //misc #define _USE_MATH_DEFINES #include // Blueberry #include #include // Qmitk #include "QmitkSimpleRegistrationView.h" // MITK #include #include #include #include #include #include #include #include #include #include #include // Qt #include #define _USE_MATH_DEFINES #include const std::string QmitkSimpleRegistrationView::VIEW_ID = "org.mitk.views.simpleregistrationview"; QmitkSimpleRegistrationView::QmitkSimpleRegistrationView() : QmitkAbstractView() , m_Controls( 0 ) , m_RegistrationType(0) { } // Destructor QmitkSimpleRegistrationView::~QmitkSimpleRegistrationView() { } void QmitkSimpleRegistrationView::StartRegistration() { QmitkRegistrationJob* pJob; if (m_Controls->m_RegBox->currentIndex()==0) { mitk::MultiModalRigidDefaultRegistrationAlgorithm< ItkFloatImageType >::Pointer algo = mitk::MultiModalRigidDefaultRegistrationAlgorithm< ItkFloatImageType >::New(); pJob = new QmitkRegistrationJob(algo); m_RegistrationType = 0; } else { mitk::MultiModalAffineDefaultRegistrationAlgorithm< ItkFloatImageType >::Pointer algo = mitk::MultiModalAffineDefaultRegistrationAlgorithm< ItkFloatImageType >::New(); pJob = new QmitkRegistrationJob(algo); m_RegistrationType = 1; } pJob->setAutoDelete(true); m_MovingImageNode = m_Controls->m_MovingImageBox->GetSelectedNode(); mitk::Image::Pointer movingImage = dynamic_cast(m_MovingImageNode->GetData()); if (mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(movingImage)) { ItkDwiType::Pointer itkVectorImagePointer = ItkDwiType::New(); mitk::CastToItkImage(movingImage, itkVectorImagePointer); itk::ExtractDwiChannelFilter< short >::Pointer filter = itk::ExtractDwiChannelFilter< short >::New(); filter->SetInput( itkVectorImagePointer); filter->SetChannelIndex(m_Controls->m_MovingChannelBox->value()); filter->Update(); mitk::Image::Pointer newImage = mitk::Image::New(); newImage->InitializeByItk( filter->GetOutput() ); newImage->SetImportChannel( filter->GetOutput()->GetBufferPointer() ); pJob->m_spMovingData = newImage; } else pJob->m_spMovingData = movingImage; mitk::Image::Pointer fixedImage = dynamic_cast(m_Controls->m_FixedImageBox->GetSelectedNode()->GetData()); if (mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(fixedImage)) { ItkDwiType::Pointer itkVectorImagePointer = ItkDwiType::New(); mitk::CastToItkImage(fixedImage, itkVectorImagePointer); itk::ExtractDwiChannelFilter< short >::Pointer filter = itk::ExtractDwiChannelFilter< short >::New(); filter->SetInput( itkVectorImagePointer); filter->SetChannelIndex(m_Controls->m_MovingChannelBox->value()); filter->Update(); mitk::Image::Pointer newImage = mitk::Image::New(); newImage->InitializeByItk( filter->GetOutput() ); newImage->SetImportChannel( filter->GetOutput()->GetBufferPointer() ); pJob->m_spTargetData = newImage; } else pJob->m_spTargetData = fixedImage; pJob->m_TargetDataUID = mitk::EnsureUID(m_Controls->m_FixedImageBox->GetSelectedNode()->GetData()); pJob->m_MovingDataUID = mitk::EnsureUID(m_Controls->m_MovingImageBox->GetSelectedNode()->GetData()); connect(pJob, SIGNAL(RegResultIsAvailable(mitk::MAPRegistrationWrapper::Pointer, const QmitkRegistrationJob*)), this, SLOT(OnRegResultIsAvailable(mitk::MAPRegistrationWrapper::Pointer, const QmitkRegistrationJob*)), Qt::BlockingQueuedConnection); QThreadPool* threadPool = QThreadPool::globalInstance(); threadPool->start(pJob); m_Controls->m_RegistrationStartButton->setEnabled(false); m_Controls->m_RegistrationStartButton->setText("Registration in progress ..."); } void QmitkSimpleRegistrationView::OnRegResultIsAvailable(mitk::MAPRegistrationWrapper::Pointer spResultRegistration, const QmitkRegistrationJob* job) { mitk::Image::Pointer movingImage = dynamic_cast(m_MovingImageNode->GetData()); mitk::Image::Pointer image; if (m_RegistrationType==0) { image = mitk::ImageMappingHelper::refineGeometry(movingImage, spResultRegistration, true); } else { if (!mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(movingImage)) { image = mitk::ImageMappingHelper::map(movingImage, spResultRegistration, false, 0, job->m_spTargetData->GetGeometry(), false, 0, mitk::ImageMappingInterpolator::BSpline_3); } else { typedef itk::ComposeImageFilter < ITKDiffusionVolumeType > ComposeFilterType; ComposeFilterType::Pointer composer = ComposeFilterType::New(); ItkDwiType::Pointer itkVectorImagePointer = mitk::DiffusionPropertyHelper::GetItkVectorImage(movingImage); for (unsigned int i=0; iGetVectorLength(); ++i) { itk::ExtractDwiChannelFilter< short >::Pointer filter = itk::ExtractDwiChannelFilter< short >::New(); filter->SetInput( itkVectorImagePointer); filter->SetChannelIndex(i); filter->Update(); mitk::Image::Pointer gradientVolume = mitk::Image::New(); gradientVolume->InitializeByItk( filter->GetOutput() ); gradientVolume->SetImportChannel( filter->GetOutput()->GetBufferPointer() ); mitk::Image::Pointer registered_mitk_image = mitk::ImageMappingHelper::map(gradientVolume, spResultRegistration, false, 0, job->m_spTargetData->GetGeometry(), false, 0, mitk::ImageMappingInterpolator::BSpline_3); ITKDiffusionVolumeType::Pointer registered_itk_image = ITKDiffusionVolumeType::New(); mitk::CastToItkImage(registered_mitk_image, registered_itk_image); composer->SetInput(i, registered_itk_image); } composer->Update(); image = mitk::GrabItkImageMemory( composer->GetOutput() ); mitk::DiffusionPropertyHelper::CopyProperties(movingImage, image, true); } } if (mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(image)) { mitk::DiffusionPropertyHelper propertyHelper( image ); propertyHelper.InitializeImage(); } mitk::DataNode::Pointer resultNode = mitk::DataNode::New(); resultNode->SetData(image); if (m_MovingImageNode.IsNotNull()) { m_MovingImageNode->SetVisibility(false); QString name = m_MovingImageNode->GetName().c_str(); if (m_RegistrationType==0) resultNode->SetName((name+"_registered (rigid)").toStdString().c_str()); else resultNode->SetName((name+"_registered (affine)").toStdString().c_str()); } else { if (m_RegistrationType==0) resultNode->SetName("Registered (rigid)"); else resultNode->SetName("Registered (affine)"); } - resultNode->SetOpacity(0.6); - resultNode->SetColor(0.0, 0.0, 1.0); +// resultNode->SetOpacity(0.6); +// resultNode->SetColor(0.0, 0.0, 1.0); GetDataStorage()->Add(resultNode); mitk::RenderingManager::GetInstance()->InitializeViews( resultNode->GetData()->GetTimeGeometry(), mitk::RenderingManager::REQUEST_UPDATE_ALL, true); if (m_Controls->m_RegOutputBox->isChecked()) { mitk::DataNode::Pointer registration_node = mitk::DataNode::New(); registration_node->SetData(spResultRegistration); if (m_RegistrationType==0) registration_node->SetName("Registration Object (rigid)"); else registration_node->SetName("Registration Object (affine)"); GetDataStorage()->Add(registration_node, resultNode); } this->GetRenderWindowPart()->RequestUpdate(); m_Controls->m_RegistrationStartButton->setEnabled(true); m_Controls->m_RegistrationStartButton->setText("Start Registration"); m_MovingImageNode = nullptr; TractoChanged(); } void QmitkSimpleRegistrationView::CreateQtPartControl( QWidget *parent ) { // build up qt view, unless already done if ( !m_Controls ) { // create GUI widgets from the Qt Designer's .ui file m_Controls = new Ui::QmitkSimpleRegistrationViewControls; m_Controls->setupUi( parent ); m_Controls->m_FixedImageBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_MovingImageBox->SetDataStorage(this->GetDataStorage()); mitk::TNodePredicateDataType::Pointer isImagePredicate = mitk::TNodePredicateDataType::New(); m_Controls->m_FixedImageBox->SetPredicate(isImagePredicate); m_Controls->m_MovingImageBox->SetPredicate(isImagePredicate); mitk::TNodePredicateDataType::Pointer isFib = mitk::TNodePredicateDataType::New(); mitk::TNodePredicateDataType::Pointer isReg = mitk::TNodePredicateDataType::New(); m_Controls->m_TractoBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_RegObjectBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_TractoBox->SetPredicate(isFib); m_Controls->m_RegObjectBox->SetPredicate(isReg); connect( m_Controls->m_FixedImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(FixedImageChanged()) ); connect( m_Controls->m_MovingImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(MovingImageChanged()) ); connect( m_Controls->m_TractoBox, SIGNAL(currentIndexChanged(int)), this, SLOT(TractoChanged()) ); connect( m_Controls->m_RegObjectBox, SIGNAL(currentIndexChanged(int)), this, SLOT(TractoChanged()) ); connect( m_Controls->m_RegistrationStartButton, SIGNAL(clicked()), this, SLOT(StartRegistration()) ); connect( m_Controls->m_TractoRegistrationStartButton, SIGNAL(clicked()), this, SLOT(StartTractoRegistration()) ); FixedImageChanged(); MovingImageChanged(); TractoChanged(); } } void QmitkSimpleRegistrationView::StartTractoRegistration() { mitk::FiberBundle::Pointer fib = dynamic_cast(m_Controls->m_TractoBox->GetSelectedNode()->GetData()); mitk::MAPRegistrationWrapper::Pointer reg = dynamic_cast(m_Controls->m_RegObjectBox->GetSelectedNode()->GetData()); mitk::MITKRegistrationHelper::Affine3DTransformType::Pointer affine = mitk::MITKRegistrationHelper::getAffineMatrix(reg, false); mitk::FiberBundle::Pointer fib_copy = fib->GetDeepCopy(); fib_copy->TransformFibers(affine); mitk::DataNode::Pointer registration_node = mitk::DataNode::New(); registration_node->SetData(fib_copy); QString name = m_Controls->m_TractoBox->GetSelectedNode()->GetName().c_str(); registration_node->SetName((name+"_registered").toStdString().c_str()); GetDataStorage()->Add(registration_node, m_Controls->m_TractoBox->GetSelectedNode()); } void QmitkSimpleRegistrationView::TractoChanged() { if (m_Controls->m_RegObjectBox->GetSelectedNode().IsNotNull() && m_Controls->m_TractoBox->GetSelectedNode().IsNotNull()) m_Controls->m_TractoRegistrationStartButton->setEnabled(true); else m_Controls->m_TractoRegistrationStartButton->setEnabled(false); } void QmitkSimpleRegistrationView::FixedImageChanged() { if (m_Controls->m_FixedImageBox->GetSelectedNode().IsNotNull()) { mitk::Image::Pointer image = dynamic_cast(m_Controls->m_FixedImageBox->GetSelectedNode()->GetData()); int channels = image->GetNumberOfChannels(); int dims = image->GetDimension(); int fourth_dim_size = image->GetTimeSteps(); bool isdiff = mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(image); if (dims==4 || channels>1) { m_Controls->m_FixedChannelBox->setEnabled(false); m_Controls->m_RegistrationStartButton->setEnabled(false); } if (isdiff) { m_Controls->m_FixedChannelBox->setEnabled(true); if (fourth_dim_size>1) m_Controls->m_FixedChannelBox->setMaximum(fourth_dim_size-1); else if (isdiff) m_Controls->m_FixedChannelBox->setMaximum(mitk::DiffusionPropertyHelper::GetGradientContainer(image)->Size()-1); } else { m_Controls->m_FixedChannelBox->setEnabled(false); } m_Controls->m_RegistrationStartButton->setEnabled(true); } else { m_Controls->m_FixedChannelBox->setEnabled(false); m_Controls->m_RegistrationStartButton->setEnabled(false); } } void QmitkSimpleRegistrationView::MovingImageChanged() { if (m_Controls->m_MovingImageBox->GetSelectedNode().IsNotNull()) { mitk::Image::Pointer image = dynamic_cast(m_Controls->m_MovingImageBox->GetSelectedNode()->GetData()); int channels = image->GetNumberOfChannels(); int dims = image->GetDimension(); int fourth_dim_size = image->GetTimeSteps(); bool isdiff = mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage(image); if (dims==4 || channels>1) { m_Controls->m_MovingChannelBox->setEnabled(false); m_Controls->m_RegistrationStartButton->setEnabled(false); } if (isdiff) { m_Controls->m_MovingChannelBox->setEnabled(true); if (fourth_dim_size>1) m_Controls->m_MovingChannelBox->setMaximum(fourth_dim_size-1); else if (isdiff) m_Controls->m_MovingChannelBox->setMaximum(mitk::DiffusionPropertyHelper::GetGradientContainer(image)->Size()-1); } else { m_Controls->m_MovingChannelBox->setEnabled(false); } m_Controls->m_RegistrationStartButton->setEnabled(true); } else { m_Controls->m_MovingChannelBox->setEnabled(false); m_Controls->m_RegistrationStartButton->setEnabled(false); } } void QmitkSimpleRegistrationView::OnSelectionChanged(berry::IWorkbenchPart::Pointer, const QList& ) { FixedImageChanged(); MovingImageChanged(); TractoChanged(); } void QmitkSimpleRegistrationView::SetFocus() { m_Controls->m_RegistrationStartButton->setFocus(); FixedImageChanged(); MovingImageChanged(); } diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkGibbsTrackingViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkGibbsTrackingViewUserManual.dox index 7f2d3dfca3..802ad0d4d5 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkGibbsTrackingViewUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkGibbsTrackingViewUserManual.dox @@ -1,38 +1,38 @@ /** \page org_mitk_views_gibbstracking Global Gibbs Tractography This view provides the user interface for the global Gibbs tractography algorithm, a global fiber tracking algorithm, originally proposed by Reisert et.al. [1]. The corresponding comman line application is named "MitkGlobalTractography". \tableofcontents \section QmitkGibbsTrackingUserManualInputData Input Data Mandatory Input: \li One ODF or tensor image selected in the datamanager Optional Input: \li Mask Image: White matter probability mask. Corresponds to the probability to generate fiber segments in the respective voxel. -\section QmitkGibbsTrackingUserManualParameters ODF Reconstruction +\section QmitkGibbsTrackingUserManualParameters Parameters \li Number of iterations: More iterations causes the algorithm to be more stable but also to take longer to finish the tracking. Recommended: minimum 10^8 iterations for full brain tractography. \li Particle length/width/weight controlling the contribution of each particle to the model M \li Start and end temperature controlling how fast the process reaches a stable state. (usually no change needed) \li Weighting between the internal (affinity of the model to long and straigt fibers) and external energy (affinity of the model towards the data). (usually no change needed). \li Minimum fiber length constraint (in mm). Shorter fibers are discarded after the tracking. The automatic selection of parameters for the particle length/width and weight are determined directly from the input image using information about the image spacing and GFA. \section QmitkGibbsTrackingUserManualTrackingSurveillance Surveilance of the tracking process Once started, the tracking can be monitored via the textual output that informs about the tracking progress and several stats of the current state of the algorithm. If enabled, the intermediate tracking results are displayed in the renderwindows each second. This live visualization should usually be disabled for performance reasons. It can be turned on and off during the tracking process via the according checkbox. The button next to this checkbox allows the visualization of only the next iteration step. \section QmitkGibbsTrackingUserManualReferences References [1] Reisert, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., Kiselev, V.: Global fiber reconstruction becomes practical. Neuroimage 54 (2011) 955-962 */ 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 677c6f915b..8c9322383e 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 @@ -1,96 +1,102 @@ /** \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 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 StrTrackUserManualTractographyPrior Tractography Prior +It is possible to use a peak image as prior for tractography on arbitrary other input images. The local progression direction is determined as the weighted average between the direction obtained from the prior and the input data. +- Weight: Weighting factor between prior and input data directions. A weight of zero means that no prior iformation is used. With a weight of one, tractography is performed directly on the prior directions itself. +- Restrict to Prior: The prior image is used as tractography mask. Voxels without prior peaks are excluded. +- New Directions from Prior: By default, the prior is used even if there is no valid direction found in the data. If unchecked, the prior cannot create directions where there are none in the data. + \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/QmitkStreamlineTrackingViewControls.ui b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingViewControls.ui index e8d893c49c..432fd0c51d 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 @@ -1,1597 +1,1597 @@ - + QmitkStreamlineTrackingViewControls 0 0 453 859 0 0 QmitkTemplate QCommandLinkButton { font-weight: normal; } QCommandLinkButton:disabled { border: none; font-weight: lighter; } QToolBox::tab { border: 1px solid #434346; font-weight: bold; } QToolBox::tab:hover { background-color: #434346; font-weight: bold; } QToolBox::tab:selected { background-color: #1c97ea; border: 1px solid #1c97ea; font-weight: bold; } QGroupBox { border: 1px solid #434346; background-color: #2d2d30; margin-top: 8px; padding-top: 8px; } QGroupBox, QGroupBox:disabled { background-color: #2d2d30; } 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> + <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 0 421 - 267 + 262 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 + 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 Qt::Vertical 20 40 0 0 435 224 ROI Constraints Specify various ROI and mask images to constrain the tractography process. - + Mask Image: Fibers that enter a region defined in this image will stop immediately. QComboBox::AdjustToMinimumContentsLength - 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 - + Endpoint Constraints: - + Stop ROI Image: The target image is used for the endpoint constraint strategy defined above. QComboBox::AdjustToMinimumContentsLength - - + Exclusion ROI Image: Fibers that leave the regions defined in this image will stop immediately. QComboBox::AdjustToMinimumContentsLength - - + Target ROI Image: Fibers that enter a region defined in this image will be discarded. QComboBox::AdjustToMinimumContentsLength - Qt::Vertical 20 40 0 0 421 - 359 + 351 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 0 435 224 Tractography Prior Restrict to Prior: Weight: Peak Image: Weighting factor between prior and data. 1.000000000000000 0.100000000000000 0.500000000000000 Restrict tractography to regions where the prior is valid. - + true - + Qt::Vertical 20 40 - New Directions From Prior: + New Directions from Prior: If unchecked, the prior cannot create directions where there are none in the data. - + - false + true 0 0 435 224 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 0 435 224 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 0 435 224 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
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