diff --git a/Modules/DiffusionCore/Algorithms/Reconstruction/itkDiffusionIntravoxelIncoherentMotionReconstructionImageFilter.h b/Modules/DiffusionCore/Algorithms/Reconstruction/itkDiffusionIntravoxelIncoherentMotionReconstructionImageFilter.h index 33e2165..b97a03c 100644 --- a/Modules/DiffusionCore/Algorithms/Reconstruction/itkDiffusionIntravoxelIncoherentMotionReconstructionImageFilter.h +++ b/Modules/DiffusionCore/Algorithms/Reconstruction/itkDiffusionIntravoxelIncoherentMotionReconstructionImageFilter.h @@ -1,403 +1,417 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center. 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 __itkDiffusionIntravoxelIncoherentMotionReconstructionImageFilter_h #define __itkDiffusionIntravoxelIncoherentMotionReconstructionImageFilter_h #include "itkImageToImageFilter.h" //#include "vnl/vnl_matrix.h" #include "vnl/vnl_vector_fixed.h" #include "vnl/vnl_matrix.h" #include "vnl/algo/vnl_svd.h" #include "itkVectorContainer.h" #include "itkVectorImage.h" //#include "QuadProg.h" #include "itkVectorImage.h" #include "vnl/vnl_least_squares_function.h" #include "vnl/vnl_cost_function.h" #include "vnl/algo/vnl_levenberg_marquardt.h" #include "vnl/vnl_math.h" #define IVIM_CEIL(val,u,o) (val) = \ ( (val) < (u) ) ? ( (u) ) : ( ( (val)>(o) ) ? ( (o) ) : ( (val) ) ); namespace itk{ /** baseclass for IVIM fitting algorithms */ struct IVIM_base { void set_measurements(const vnl_vector& x) { measurements.set_size(x.size()); measurements.copy_in(x.data_block()); } void set_bvalues(const vnl_vector& x) { bvalues.set_size(x.size()); bvalues.copy_in(x.data_block()); } vnl_vector measurements; vnl_vector bvalues; int N; }; /** Fitt all three parameters */ struct IVIM_3param : public IVIM_base, vnl_least_squares_function { IVIM_3param(unsigned int number_of_measurements) : vnl_least_squares_function(3, number_of_measurements, no_gradient) { N = get_number_of_residuals(); } void f(const vnl_vector& x, vnl_vector& fx) override { double ef = x[0]; double D = x[1]; double Dstar = x[2]; for(int s=0; s0.003) + fx[s] += D*100000; + if (Dstar>0.3) + fx[s] += Dstar*100000; } } }; /** fit by setting DStar to a fix value */ struct IVIM_fixdstar : public IVIM_base, vnl_least_squares_function { IVIM_fixdstar(unsigned int number_of_measurements, double DStar) : vnl_least_squares_function(2, number_of_measurements, no_gradient) { N = get_number_of_residuals(); fixDStar = DStar; } void f(const vnl_vector& x, vnl_vector& fx) override { double ef = x[0]; double D = x[1]; for(int s=0; s0.003) + fx[s] += D*100000; } } double fixDStar; }; /** fit a monoexponential curve only estimating D and f */ struct IVIM_d_and_f : public IVIM_base, vnl_least_squares_function { IVIM_d_and_f(unsigned int number_of_measurements) : vnl_least_squares_function(2, number_of_measurements, no_gradient) { N = get_number_of_residuals(); } void f(const vnl_vector& x, vnl_vector& fx) override { double D = x[0]; double f = x[1]; for(int s=0; s0.003) + fx[s] += D*100000; } } }; /** fiting DStar and f with fix value of D */ struct IVIM_fixd : public IVIM_base, vnl_least_squares_function { IVIM_fixd(unsigned int number_of_measurements, double D) : vnl_least_squares_function(2, number_of_measurements, no_gradient) { N = get_number_of_residuals(); fixD = D; } void f(const vnl_vector& x, vnl_vector& fx) override { double ef = x[0]; double Dstar = x[1]; for(int s=0; s0.3) + fx[s] += Dstar*100000; } } double fixD; }; /** fiting DStar with given f and D */ struct IVIM_dstar_only : public IVIM_base, vnl_least_squares_function { IVIM_dstar_only(unsigned int number_of_measurements, double D, double f) : vnl_least_squares_function(1, number_of_measurements, no_gradient) { N = get_number_of_residuals(); fixD = D; fixF = f; } void f(const vnl_vector& x, vnl_vector& fx) override { double Dstar = x[0]; for(int s=0; s0.3) + fx[s] += Dstar*100000; } } double fixD; double fixF; }; struct MeasAndBvals { vnl_vector meas; vnl_vector bvals; int N; }; /** \class DiffusionIntravoxelIncoherentMotionReconstructionImageFilter */ template< class TInputPixelType, class TOutputPixelType> class DiffusionIntravoxelIncoherentMotionReconstructionImageFilter : public ImageToImageFilter< VectorImage< TInputPixelType, 3 >, Image< TOutputPixelType, 3 > > { public: struct IVIMSnapshot { double currentF; double currentFunceiled; double currentD; double currentDStar; bool iterated_sequence; // wether each measurement has its own b0-acqu. std::vector baselineind; // baseline image indicies std::vector gradientind; // gradient image indicies vnl_vector bvalues; // bvalues != 0 int num_weighted; // total number of measurements vnl_vector meas_for_threshold; // all measurements, thresholded blanked out vnl_vector allmeas; // all measurements int num_high; // number of used measurements std::vector high_indices; // indices of used measurements vnl_vector high_bvalues; // bvals of used measurements vnl_vector high_meas; // used measurements // fit 1 vnl_vector meas1; vnl_vector bvals1; // fit 2 vnl_vector meas2; vnl_vector bvals2; }; enum IVIM_Method { IVIM_FIT_ALL, IVIM_DSTAR_FIX, IVIM_D_THEN_DSTAR, IVIM_LINEAR_D_THEN_F, IVIM_REGULARIZED }; typedef DiffusionIntravoxelIncoherentMotionReconstructionImageFilter Self; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; typedef ImageToImageFilter< VectorImage< TInputPixelType, 3>, Image< TOutputPixelType,3 > > Superclass; /** Method for creation through the object factory. */ itkFactorylessNewMacro(Self) itkCloneMacro(Self) /** Runtime information support. */ itkTypeMacro(DiffusionIntravoxelIncoherentMotionReconstructionImageFilter, ImageToImageFilter); typedef TOutputPixelType OutputPixelType; typedef TInputPixelType InputPixelType; /** Reference image data, This image is aquired in the absence * of a diffusion sensitizing field gradient */ typedef typename Superclass::InputImageType InputImageType; typedef Image< OutputPixelType, 3 > OutputImageType; typedef typename Superclass::OutputImageRegionType OutputImageRegionType; /** Holds each magnetic field gradient used to acquire one DWImage */ typedef vnl_vector_fixed< double, 3 > GradientDirectionType; /** Container to hold gradient directions of the 'n' DW measurements */ typedef VectorContainer< unsigned int, GradientDirectionType > GradientDirectionContainerType; // vector image typedefs for regularized fit typedef itk::VectorImage VectorImageType; typedef itk::Image, 3> InitialFitImageType; /** set method to add gradient directions and its corresponding * image. The image here is a VectorImage. The user is expected to pass the * gradient directions in a container. The ith element of the container * corresponds to the gradient direction of the ith component image the * VectorImage. For the baseline image, a vector of all zeros * should be set.*/ void SetGradientDirections( GradientDirectionContainerType * ); void SetBValue(double bval){m_BValue = bval;} void SetBThres(double bval){m_BThres = bval;} void SetS0Thres(double val){m_S0Thres = val;} void SetDStar(double dstar){m_DStar = dstar;} void SetFitDStar(bool fit){m_FitDStar = fit;} void SetVerbose(bool verbose){m_Verbose = verbose;} void SetNumberIterations(int num){m_NumberIterations = num;} void SetLambda(double lambda){m_Lambda = lambda;} void SetCrossPosition(typename InputImageType::IndexType crosspos){this->m_CrossPosition = crosspos;} void SetMethod(IVIM_Method method){m_Method = method;} IVIMSnapshot GetSnapshot(){return m_Snap;} /** Return the gradient direction. idx is 0 based */ virtual GradientDirectionType GetGradientDirection( unsigned int idx) const { if( idx >= m_NumberOfGradientDirections ) { itkExceptionMacro( << "Gradient direction " << idx << "does not exist" ); } return m_GradientDirectionContainer->ElementAt( idx+1 ); } protected: DiffusionIntravoxelIncoherentMotionReconstructionImageFilter(); ~DiffusionIntravoxelIncoherentMotionReconstructionImageFilter() {}; void PrintSelf(std::ostream& os, Indent indent) const; void BeforeThreadedGenerateData(); void ThreadedGenerateData( const OutputImageRegionType &outputRegionForThread, ThreadIdType); void AfterThreadedGenerateData(); MeasAndBvals ApplyS0Threshold(vnl_vector &meas, vnl_vector &bvals); private: double myround(double number); /** container to hold gradient directions */ GradientDirectionContainerType::Pointer m_GradientDirectionContainer; /** Number of gradient measurements */ unsigned int m_NumberOfGradientDirections; double m_BValue; double m_BThres; double m_S0Thres; double m_DStar; IVIM_Method m_Method; typename OutputImageType::Pointer m_DMap; typename OutputImageType::Pointer m_DStarMap; bool m_FitDStar; IVIMSnapshot m_Snap; bool m_Verbose; typename VectorImageType::Pointer m_InternalVectorImage; typename InitialFitImageType::Pointer m_InitialFitImage; int m_NumberIterations; // for total variation vnl_vector m_tmp_allmeas; double m_Lambda; typename InputImageType::IndexType m_CrossPosition; }; } #ifndef ITK_MANUAL_INSTANTIATION #include "itkDiffusionIntravoxelIncoherentMotionReconstructionImageFilter.cpp" #endif #endif //__itkDiffusionIntravoxelIncoherentMotionReconstructionImageFilter_h diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/documentation/UserManual/QmitkUserIVIMViewManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/documentation/UserManual/QmitkUserIVIMViewManual.dox index b2450c1..958f4c6 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/documentation/UserManual/QmitkUserIVIMViewManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/documentation/UserManual/QmitkUserIVIMViewManual.dox @@ -1,34 +1,34 @@ /** \page org_mitk_views_ivim Intra-voxel incoherent motion estimation (IVIM) This view enables the estimation of "Intra-voxel incoherent motion estimation" (IVIM) and diffusion kurtosis parameters from diffusion weighted images. The required input is a diffusion weighted image that was acquired with multiple different b-values (unidirectional). The view enables an interactive exploration of the selected image (click around in the image and watch the estimated parameters in the figure of the view) as well as generation of parameter maps. \section QmitkIVIMViewAdvanced IVIM fit methods -The IVIM model is a bi-exponential model defined by the parameters f, D and D*: \f$(1-f)e^{-bD}+fe^{-b(D+D^*)}\f$. All fit methods available in the view ultimatle use this model but they differ how the three parameters are estimated: +The IVIM model is a bi-exponential model defined by the parameters f, D and D*: (1-f)e^{-bD}+fe^{-b(D+D^*)}. All fit methods available in the view ultimatle use this model but they differ how the three parameters are estimated: \li Jointly fit D, f and D*: All three parameters are estimated simultaneously. \li Fit D & f with fixed D* value: Only f and D are estimated, D* is fixed (user defined). -\li Fit D & f (high b), then fit D*: First fit f and D (monoexponentially \f$(1-f)e^{-bD}\f$) and use these then fixed parameters in a second fit of D* with the complete bi-exponential model. +\li Fit D & f (high b), then fit D*: First fit f and D (monoexponentially (1-f)e^{-bD}) and use these then fixed parameters in a second fit of D* with the complete bi-exponential model. \li Linearly fit D & f (high b), then fit D*: First fit f and D linearly and use these then fixed parameters in a second fit of D* with the complete bi-exponential model. Negative values for D and D* are penalized during the fit. \section QmitkIVIMViewReferences Suggested Readings Toward an optimal distribution of b values for intravoxel incoherent motion imaging. Lemke A, Stieltjes B, Schad LR, Laun FB. Magn Reson Imaging. 2011 Jul;29(6):766-76. Epub 2011 May 5. PMID: 21549538 Differentiation of pancreas carcinoma from healthy pancreatic tissue using multiple b-values: comparison of apparent diffusion coefficient and intravoxel incoherent motion derived parameters. Lemke A, Laun FB, Klauss M, Re TJ, Simon D, Delorme S, Schad LR, Stieltjes B. Invest Radiol. 2009 Dec;44(12):769-75. PMID: 19838121 */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/src/internal/QmitkIVIMView.cpp b/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/src/internal/QmitkIVIMView.cpp index c6bfd96..aa5bb54 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/src/internal/QmitkIVIMView.cpp +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/src/internal/QmitkIVIMView.cpp @@ -1,1076 +1,1095 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center. 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 // Qmitk #include "QmitkIVIMView.h" // qt #include "qmessagebox.h" #include "qclipboard.h" // mitk #include "mitkImage.h" #include "mitkImageCast.h" #include "mitkLookupTable.h" #include "mitkLookupTableProperty.h" #include #include // itk #include "itkScalarImageToHistogramGenerator.h" #include "itkRegionOfInterestImageFilter.h" #include "itkImageRegionConstIteratorWithIndex.h" // itk/mitk #include "itkDiffusionIntravoxelIncoherentMotionReconstructionImageFilter.h" #include "itkRegularizedIVIMReconstructionFilter.h" #include "mitkImageCast.h" #include #include #include #include #include #include +#include const std::string QmitkIVIMView::VIEW_ID = "org.mitk.views.ivim"; QmitkIVIMView::QmitkIVIMView() : QmitkAbstractView() , m_Controls( 0 ) , m_Active(false) , m_Visible(false) , m_HoldUpdate(false) { } QmitkIVIMView::~QmitkIVIMView() { } void QmitkIVIMView::CreateQtPartControl( QWidget *parent ) { // hold update untill all elements are set this->m_HoldUpdate = true; // build up qt view, unless already done if ( !m_Controls ) { // create GUI widgets from the Qt Designer's .ui file m_Controls = new Ui::QmitkIVIMViewControls; m_Controls->setupUi( parent ); connect( m_Controls->m_ButtonStart, SIGNAL(clicked()), this, SLOT(FittIVIMStart()) ); connect( m_Controls->m_ButtonAutoThres, SIGNAL(clicked()), this, SLOT(AutoThreshold()) ); connect( m_Controls->m_MethodCombo, SIGNAL(currentIndexChanged(int)), this, SLOT(OnIvimFitChanged(int)) ); connect( m_Controls->m_DStarSlider, SIGNAL(valueChanged(int)), this, SLOT(DStarSlider(int)) ); connect( m_Controls->m_BThreshSlider, SIGNAL(valueChanged(int)), this, SLOT(BThreshSlider(int)) ); connect( m_Controls->m_S0ThreshSlider, SIGNAL(valueChanged(int)), this, SLOT(S0ThreshSlider(int)) ); connect( m_Controls->m_NumItSlider, SIGNAL(valueChanged(int)), this, SLOT(NumItsSlider(int)) ); connect( m_Controls->m_LambdaSlider, SIGNAL(valueChanged(int)), this, SLOT(LambdaSlider(int)) ); connect( m_Controls->m_CheckDStar, SIGNAL(clicked()), this, SLOT(Checkbox()) ); connect( m_Controls->m_CheckD, SIGNAL(clicked()), this, SLOT(Checkbox()) ); connect( m_Controls->m_Checkf, SIGNAL(clicked()), this, SLOT(Checkbox()) ); connect( m_Controls->m_CurveClipboard, SIGNAL(clicked()), this, SLOT(ClipboardCurveButtonClicked()) ); connect( m_Controls->m_ValuesClipboard, SIGNAL(clicked()), this, SLOT(ClipboardStatisticsButtonClicked()) ); connect( m_Controls->m_SavePlot, SIGNAL(clicked()), this, SLOT(SavePlotButtonClicked()) ); // connect all kurtosis actions to a recompute connect( m_Controls->m_KurtosisRangeWidget, SIGNAL( rangeChanged(double, double)), this, SLOT(OnKurtosisParamsChanged() ) ); connect( m_Controls->m_OmitBZeroCB, SIGNAL( stateChanged(int) ), this, SLOT( OnKurtosisParamsChanged() ) ); connect( m_Controls->m_KurtosisFitScale, SIGNAL( currentIndexChanged(int)), this, SLOT( OnKurtosisParamsChanged() ) ); connect( m_Controls->m_UseKurtosisBoundsCB, SIGNAL(clicked() ), this, SLOT( OnKurtosisParamsChanged() ) ); m_Controls->m_DwiBox->SetDataStorage(this->GetDataStorage()); mitk::NodePredicateIsDWI::Pointer isDwi = mitk::NodePredicateIsDWI::New(); m_Controls->m_DwiBox->SetPredicate( isDwi ); connect( (QObject*)(m_Controls->m_DwiBox), SIGNAL(currentIndexChanged(int)), this, SLOT(UpdateGui())); m_Controls->m_MaskBox->SetDataStorage(this->GetDataStorage()); m_Controls->m_MaskBox->SetZeroEntryText("--"); mitk::TNodePredicateDataType::Pointer isImagePredicate = mitk::TNodePredicateDataType::New(); mitk::NodePredicateProperty::Pointer isBinaryPredicate = mitk::NodePredicateProperty::New("binary", mitk::BoolProperty::New(true)); mitk::NodePredicateDimension::Pointer is3D = mitk::NodePredicateDimension::New(3); m_Controls->m_MaskBox->SetPredicate( mitk::NodePredicateAnd::New(isBinaryPredicate, mitk::NodePredicateAnd::New(isImagePredicate, is3D)) ); connect( (QObject*)(m_Controls->m_MaskBox), SIGNAL(currentIndexChanged(int)), this, SLOT(UpdateGui())); connect( (QObject*)(m_Controls->m_ModelTabSelectionWidget), SIGNAL(currentChanged(int)), this, SLOT(OnModelTabChanged(int))); } QString dstar = QString::number(m_Controls->m_DStarSlider->value()/1000.0); m_Controls->m_DStarLabel->setText(dstar); QString bthresh = QString::number(m_Controls->m_BThreshSlider->value()*5.0); m_Controls->m_BThreshLabel->setText(bthresh); QString s0thresh = QString::number(m_Controls->m_S0ThreshSlider->value()*0.5); m_Controls->m_S0ThreshLabel->setText(s0thresh); QString numits = QString::number(m_Controls->m_NumItSlider->value()); m_Controls->m_NumItsLabel->setText(numits); QString lambda = QString::number(m_Controls->m_LambdaSlider->value()*.00001); m_Controls->m_LambdaLabel->setText(lambda); m_Controls->m_Warning->setVisible(false); OnIvimFitChanged(m_Controls->m_MethodCombo->currentIndex()); m_Controls->m_KurtosisRangeWidget->setSingleStep(0.1); m_Controls->m_KurtosisRangeWidget->setRange( 0.0, 10.0 ); m_Controls->m_KurtosisRangeWidget->setMaximumValue( 5.0 ); // LogScale not working yet, have to fix that first // m_Controls->m_KurtosisFitScale->setEnabled(false); //m_Controls->m_MaximalBValueWidget->setVisible( false ); // release update block after the UI-elements were all set this->m_HoldUpdate = false; QmitkIVIMView::InitChartIvim(); m_ListenerActive = false; if (this->GetRenderWindowPart()) { m_SliceChangeListener.RenderWindowPartActivated(this->GetRenderWindowPart()); connect(&m_SliceChangeListener, SIGNAL(SliceChanged()), this, SLOT(OnSliceChanged())); m_ListenerActive = true; } } void QmitkIVIMView::OnModelTabChanged(int tab) { if (tab==0) InitChartIvim(); else if (tab==1) InitChartKurtosis(); UpdateGui(); } void QmitkIVIMView::AddSecondFitPlot() { if (m_Controls->m_ChartWidget->GetDataElementByLabel("signal values (used for second fit)") == nullptr) { std::vector< std::pair > init_data; m_Controls->m_ChartWidget->AddData2D(init_data, "signal values (used for second fit)", QmitkChartWidget::ChartType::scatter); m_Controls->m_ChartWidget->SetColor("signal values (used for second fit)", "white"); m_Controls->m_ChartWidget->SetMarkerSymbol("signal values (used for second fit)", QmitkChartWidget::MarkerSymbol::x_thin); m_Controls->m_ChartWidget->Show(true); m_Controls->m_ChartWidget->SetShowDataPoints(false); m_Controls->m_ChartWidget->SetShowSubchart(false); } } void QmitkIVIMView::RemoveSecondFitPlot() { if (m_Controls->m_ChartWidget->GetDataElementByLabel("signal values (used for second fit)") != nullptr) { m_Controls->m_ChartWidget->RemoveData("signal values (used for second fit)"); m_Controls->m_ChartWidget->Show(true); m_Controls->m_ChartWidget->SetShowDataPoints(false); m_Controls->m_ChartWidget->SetShowSubchart(false); } } void QmitkIVIMView::InitChartIvim() { m_Controls->m_ChartWidget->Clear(); std::vector< std::pair > init_data; m_Controls->m_ChartWidget->AddData2D(init_data, "D-part of fitted model", QmitkChartWidget::ChartType::line); m_Controls->m_ChartWidget->AddData2D(init_data, "fitted model", QmitkChartWidget::ChartType::line); m_Controls->m_ChartWidget->AddData2D(init_data, "signal values", QmitkChartWidget::ChartType::scatter); m_Controls->m_ChartWidget->SetColor("fitted model", "red"); m_Controls->m_ChartWidget->SetColor("signal values", "white"); m_Controls->m_ChartWidget->SetColor("D-part of fitted model", "cyan"); m_Controls->m_ChartWidget->SetYAxisScale(QmitkChartWidget::AxisScale::log); m_Controls->m_ChartWidget->SetYAxisLabel("S/S0"); m_Controls->m_ChartWidget->SetXAxisLabel("b-value"); m_Controls->m_ChartWidget->SetLineStyle("fitted model", QmitkChartWidget::LineStyle::solid); m_Controls->m_ChartWidget->SetLineStyle("D-part of fitted model", QmitkChartWidget::LineStyle::dashed); m_Controls->m_ChartWidget->SetMarkerSymbol("signal values", QmitkChartWidget::MarkerSymbol::diamond); m_Controls->m_ChartWidget->Show(true); m_Controls->m_ChartWidget->SetShowDataPoints(false); m_Controls->m_ChartWidget->SetShowSubchart(false); } void QmitkIVIMView::InitChartKurtosis() { m_Controls->m_ChartWidget->Clear(); std::vector< std::pair > init_data; m_Controls->m_ChartWidget->AddData2D(init_data, "D-part of fitted model", QmitkChartWidget::ChartType::line); m_Controls->m_ChartWidget->AddData2D(init_data, "fitted model", QmitkChartWidget::ChartType::line); m_Controls->m_ChartWidget->AddData2D(init_data, "signal values", QmitkChartWidget::ChartType::scatter); m_Controls->m_ChartWidget->SetColor("fitted model", "red"); m_Controls->m_ChartWidget->SetColor("signal values", "white"); m_Controls->m_ChartWidget->SetColor("D-part of fitted model", "cyan"); m_Controls->m_ChartWidget->SetYAxisScale(QmitkChartWidget::AxisScale::log); m_Controls->m_ChartWidget->SetYAxisLabel("S"); m_Controls->m_ChartWidget->SetXAxisLabel("b-value"); m_Controls->m_ChartWidget->SetLineStyle("fitted model", QmitkChartWidget::LineStyle::solid); m_Controls->m_ChartWidget->SetLineStyle("D-part of fitted model", QmitkChartWidget::LineStyle::dashed); m_Controls->m_ChartWidget->SetMarkerSymbol("signal values", QmitkChartWidget::MarkerSymbol::diamond); m_Controls->m_ChartWidget->Show(true); m_Controls->m_ChartWidget->SetShowDataPoints(false); m_Controls->m_ChartWidget->SetShowSubchart(false); } void QmitkIVIMView::SetFocus() { m_Controls->m_ButtonAutoThres->setFocus(); } void QmitkIVIMView::Checkbox() { OnSliceChanged(); } void QmitkIVIMView::OnIvimFitChanged(int val) { switch(val) { case 0: m_Controls->m_DstarFrame->setVisible(false); m_Controls->m_NeglSiFrame->setVisible(true); m_Controls->m_NeglBframe->setVisible(false); m_Controls->m_IterationsFrame->setVisible(false); m_Controls->m_LambdaFrame->setVisible(false); break; case 1: m_Controls->m_DstarFrame->setVisible(true); m_Controls->m_NeglSiFrame->setVisible(true); m_Controls->m_NeglBframe->setVisible(false); m_Controls->m_IterationsFrame->setVisible(false); m_Controls->m_LambdaFrame->setVisible(false); break; case 2: m_Controls->m_DstarFrame->setVisible(false); m_Controls->m_NeglSiFrame->setVisible(true); m_Controls->m_NeglBframe->setVisible(true); m_Controls->m_IterationsFrame->setVisible(false); m_Controls->m_LambdaFrame->setVisible(false); break; case 3: m_Controls->m_DstarFrame->setVisible(false); m_Controls->m_NeglSiFrame->setVisible(true); m_Controls->m_NeglBframe->setVisible(true); m_Controls->m_IterationsFrame->setVisible(false); m_Controls->m_LambdaFrame->setVisible(false); break; case 4: m_Controls->m_DstarFrame->setVisible(false); m_Controls->m_NeglSiFrame->setVisible(false); m_Controls->m_NeglBframe->setVisible(false); m_Controls->m_IterationsFrame->setVisible(false); m_Controls->m_LambdaFrame->setVisible(false); break; } OnSliceChanged(); } void QmitkIVIMView::DStarSlider (int val) { QString sval = QString::number(val/1000.0); m_Controls->m_DStarLabel->setText(sval); OnSliceChanged(); } void QmitkIVIMView::BThreshSlider (int val) { QString sval = QString::number(val*5.0); m_Controls->m_BThreshLabel->setText(sval); OnSliceChanged(); } void QmitkIVIMView::S0ThreshSlider (int val) { QString sval = QString::number(val*0.5); m_Controls->m_S0ThreshLabel->setText(sval); OnSliceChanged(); } void QmitkIVIMView::NumItsSlider (int val) { QString sval = QString::number(val); m_Controls->m_NumItsLabel->setText(sval); OnSliceChanged(); } void QmitkIVIMView::LambdaSlider (int val) { QString sval = QString::number(val*.00001); m_Controls->m_LambdaLabel->setText(sval); OnSliceChanged(); } void QmitkIVIMView::UpdateGui() { m_Controls->m_FittedParamsLabel->setText(""); if (m_Controls->m_DwiBox->GetSelectedNode().IsNotNull()) { m_Controls->m_ChartWidget->setVisible(true); m_HoldUpdate = false; } else { m_Controls->m_ChartWidget->setVisible(false); } m_Controls->m_ButtonStart->setEnabled( m_Controls->m_DwiBox->GetSelectedNode().IsNotNull() ); m_Controls->m_ButtonAutoThres->setEnabled( m_Controls->m_DwiBox->GetSelectedNode().IsNotNull() ); m_Controls->m_ControlsFrame->setEnabled( m_Controls->m_DwiBox->GetSelectedNode().IsNotNull() ); m_Controls->m_BottomControlsFrame->setEnabled( m_Controls->m_DwiBox->GetSelectedNode().IsNotNull() ); OnSliceChanged(); } void QmitkIVIMView::OnSelectionChanged(berry::IWorkbenchPart::Pointer /*part*/, const QList& ) { // UpdateGui(); } void QmitkIVIMView::AutoThreshold() { if (m_Controls->m_DwiBox->GetSelectedNode().IsNull()) { // Nothing selected. Inform the user and return QMessageBox::information( nullptr, "Template", "Please load and select a diffusion image before starting image processing."); return; } mitk::Image* dimg = dynamic_cast(m_Controls->m_DwiBox->GetSelectedNode()->GetData()); if (!dimg) { // Nothing selected. Inform the user and return QMessageBox::information( nullptr, "Template", "No valid diffusion image was found."); return; } // find bzero index int index = -1; auto directions = mitk::DiffusionPropertyHelper::GetGradientContainer(dimg); for(DirContainerType::ConstIterator it = directions->Begin(); it != directions->End(); ++it) { index++; GradientDirectionType g = it.Value(); if(g[0] == 0 && g[1] == 0 && g[2] == 0 ) break; } VecImgType::Pointer vecimg = VecImgType::New(); mitk::CastToItkImage(dimg, vecimg); int vecLength = vecimg->GetVectorLength(); index = index > vecLength-1 ? vecLength-1 : index; MITK_INFO << "Performing Histogram Analysis on Channel" << index; typedef itk::Image ImgType; ImgType::Pointer img = ImgType::New(); mitk::CastToItkImage(dimg, img); itk::ImageRegionIterator itw (img, img->GetLargestPossibleRegion() ); itw.GoToBegin(); itk::ImageRegionConstIterator itr (vecimg, vecimg->GetLargestPossibleRegion() ); itr.GoToBegin(); while(!itr.IsAtEnd()) { itw.Set(itr.Get().GetElement(index)); ++itr; ++itw; } typedef itk::Statistics::ScalarImageToHistogramGenerator< ImgType > HistogramGeneratorType; typedef HistogramGeneratorType::HistogramType HistogramType; HistogramGeneratorType::Pointer histogramGenerator = HistogramGeneratorType::New(); histogramGenerator->SetInput( img ); histogramGenerator->SetMarginalScale( 10 ); // Defines y-margin width of histogram histogramGenerator->SetNumberOfBins( 100 ); // CT range [-1024, +2048] --> bin size 4 values histogramGenerator->SetHistogramMin( dimg->GetStatistics()->GetScalarValueMin() ); histogramGenerator->SetHistogramMax( dimg->GetStatistics()->GetScalarValueMax() * .5 ); histogramGenerator->Compute(); HistogramType::ConstIterator iter = histogramGenerator->GetOutput()->Begin(); float maxFreq = 0; float maxValue = 0; while ( iter != histogramGenerator->GetOutput()->End() ) { if(iter.GetFrequency() > maxFreq) { maxFreq = iter.GetFrequency(); maxValue = iter.GetMeasurementVector()[0]; } ++iter; } maxValue *= 2; int sliderPos = maxValue * 2; m_Controls->m_S0ThreshSlider->setValue(sliderPos); S0ThreshSlider(sliderPos); } void QmitkIVIMView::FittIVIMStart() { if (m_Controls->m_DwiBox->GetSelectedNode().IsNull()) { QMessageBox::information( nullptr, "Template", "No valid diffusion-weighted image selected."); return; } mitk::Image* img = dynamic_cast(m_Controls->m_DwiBox->GetSelectedNode()->GetData()); VecImgType::Pointer vecimg = VecImgType::New(); mitk::CastToItkImage(img, vecimg); OutImgType::IndexType dummy; if( m_Controls->m_ModelTabSelectionWidget->currentIndex() ) { // KURTOSIS KurtosisFilterType::Pointer filter = KurtosisFilterType::New(); filter->SetInput(vecimg); filter->SetReferenceBValue(mitk::DiffusionPropertyHelper::GetReferenceBValue(img)); filter->SetGradientDirections(mitk::DiffusionPropertyHelper::GetGradientContainer(img)); filter->SetSmoothingSigma( m_Controls->m_SigmaSpinBox->value() ); if( m_Controls->m_UseKurtosisBoundsCB->isChecked() ) filter->SetBoundariesForKurtosis( m_Controls->m_KurtosisRangeWidget->minimumValue(), m_Controls->m_KurtosisRangeWidget->maximumValue() ); filter->SetFittingScale( static_cast(m_Controls->m_KurtosisFitScale->currentIndex() ) ); if( m_Controls->m_MaskBox->GetSelectedNode().IsNotNull() ) { mitk::Image::Pointer maskImg = dynamic_cast(m_Controls->m_MaskBox->GetSelectedNode()->GetData()); typedef itk::Image MaskImgType; MaskImgType::Pointer maskItk; CastToItkImage( maskImg, maskItk ); filter->SetImageMask( maskItk ); } filter->Update(); mitk::LookupTable::Pointer kurt_map_lut = mitk::LookupTable::New(); kurt_map_lut->SetType( mitk::LookupTable::JET ); mitk::LookupTableProperty::Pointer kurt_lut_prop = mitk::LookupTableProperty::New(); kurt_lut_prop->SetLookupTable( kurt_map_lut ); mitk::Image::Pointer dimage = mitk::Image::New(); dimage->InitializeByItk( filter->GetOutput(0) ); dimage->SetVolume( filter->GetOutput(0)->GetBufferPointer()); mitk::Image::Pointer kimage = mitk::Image::New(); kimage->InitializeByItk( filter->GetOutput(1) ); kimage->SetVolume( filter->GetOutput(1)->GetBufferPointer()); QString new_dname = "Kurtosis_DMap"; new_dname.append("_Method-"+m_Controls->m_KurtosisFitScale->currentText()); QString new_kname = "Kurtosis_KMap"; new_kname.append("_Method-"+m_Controls->m_KurtosisFitScale->currentText()); if( m_Controls->m_CheckKurtD->isChecked() ) { mitk::DataNode::Pointer dnode = mitk::DataNode::New(); dnode->SetData( dimage ); dnode->SetName(new_dname.toLatin1()); dnode->SetProperty("LookupTable", kurt_lut_prop ); GetDataStorage()->Add(dnode, m_Controls->m_DwiBox->GetSelectedNode()); } if( m_Controls->m_CheckKurtK->isChecked() ) { mitk::DataNode::Pointer knode = mitk::DataNode::New(); knode->SetData( kimage ); knode->SetName(new_kname.toLatin1()); knode->SetProperty("LookupTable", kurt_lut_prop ); GetDataStorage()->Add(knode, m_Controls->m_DwiBox->GetSelectedNode()); } } else { FittIVIM(vecimg, mitk::DiffusionPropertyHelper::GetGradientContainer(img), mitk::DiffusionPropertyHelper::GetReferenceBValue(img), true, dummy); OutputToDatastorage(m_Controls->m_DwiBox->GetSelectedNode()); } } void QmitkIVIMView::OnKurtosisParamsChanged() { OnSliceChanged(); } void QmitkIVIMView::OnSliceChanged() { if(m_HoldUpdate || !m_Visible) return; m_Controls->m_Warning->setVisible(false); if(m_Controls->m_DwiBox->GetSelectedNode().IsNull()) return; mitk::Image::Pointer diffusionImg = dynamic_cast(m_Controls->m_DwiBox->GetSelectedNode()->GetData()); mitk::Image::Pointer maskImg = nullptr; if (m_Controls->m_MaskBox->GetSelectedNode().IsNotNull()) maskImg = dynamic_cast(m_Controls->m_MaskBox->GetSelectedNode()->GetData()); if (!this->GetRenderWindowPart()) return; if (!m_ListenerActive) { m_SliceChangeListener.RenderWindowPartActivated(this->GetRenderWindowPart()); connect(&m_SliceChangeListener, SIGNAL(SliceChanged()), this, SLOT(OnSliceChanged())); m_ListenerActive = true; } VecImgType::Pointer vecimg = VecImgType::New(); mitk::CastToItkImage(diffusionImg, vecimg); VecImgType::Pointer roiImage = VecImgType::New(); if(maskImg.IsNull()) { int roisize = 0; if(m_Controls->m_MethodCombo->currentIndex() == 4) roisize = 3; mitk::Point3D pos = this->GetRenderWindowPart()->GetSelectedPosition(); VecImgType::IndexType crosspos; diffusionImg->GetGeometry()->WorldToIndex(pos, crosspos); if (!vecimg->GetLargestPossibleRegion().IsInside(crosspos)) { m_Controls->m_Warning->setText(QString("Crosshair position not inside of selected diffusion weighted image. Reinit needed!")); m_Controls->m_Warning->setVisible(true); return; } else m_Controls->m_Warning->setVisible(false); VecImgType::IndexType index; index[0] = crosspos[0] - roisize; index[0] = index[0] < 0 ? 0 : index[0]; index[1] = crosspos[1] - roisize; index[1] = index[1] < 0 ? 0 : index[1]; index[2] = crosspos[2] - roisize; index[2] = index[2] < 0 ? 0 : index[2]; VecImgType::SizeType size; size[0] = roisize*2+1; size[1] = roisize*2+1; size[2] = roisize*2+1; VecImgType::SizeType maxSize = vecimg->GetLargestPossibleRegion().GetSize(); size[0] = index[0]+size[0] > maxSize[0] ? maxSize[0]-index[0] : size[0]; size[1] = index[1]+size[1] > maxSize[1] ? maxSize[1]-index[1] : size[1]; size[2] = index[2]+size[2] > maxSize[2] ? maxSize[2]-index[2] : size[2]; VecImgType::RegionType region; region.SetSize( size ); region.SetIndex( index ); vecimg->SetRequestedRegion( region ); VecImgType::IndexType newstart; newstart.Fill(0); VecImgType::RegionType newregion; newregion.SetSize( size ); newregion.SetIndex( newstart ); roiImage->CopyInformation( vecimg ); roiImage->SetRegions( newregion ); roiImage->SetOrigin( pos ); roiImage->Allocate(); // roiImage->SetPixel(newstart, vecimg->GetPixel(index)); typedef itk::ImageRegionIterator VectorIteratorType; VectorIteratorType vecit(vecimg, vecimg->GetRequestedRegion() ); vecit.GoToBegin(); typedef itk::ImageRegionIterator VectorIteratorType; VectorIteratorType vecit2(roiImage, roiImage->GetLargestPossibleRegion() ); vecit2.GoToBegin(); while( !vecit.IsAtEnd() ) { vecit2.Set(vecit.Get()); ++vecit; ++vecit2; } if( m_Controls->m_ModelTabSelectionWidget->currentIndex() ) { FitKurtosis(roiImage, mitk::DiffusionPropertyHelper::GetGradientContainer(diffusionImg), mitk::DiffusionPropertyHelper::GetReferenceBValue(diffusionImg), newstart); } else { FittIVIM(roiImage, mitk::DiffusionPropertyHelper::GetGradientContainer(diffusionImg), mitk::DiffusionPropertyHelper::GetReferenceBValue(diffusionImg), false, crosspos); } } else { typedef itk::Image MaskImgType; MaskImgType::Pointer maskItk; CastToItkImage( maskImg, maskItk ); mitk::Point3D pos; pos[0] = 0; pos[1] = 0; pos[2] = 0; VecImgType::IndexType index; index[0] = 0; index[1] = 0; index[2] = 0; VecImgType::SizeType size; size[0] = 1; size[1] = 1; size[2] = 1; VecImgType::RegionType region; region.SetSize( size ); region.SetIndex( index ); vecimg->SetRequestedRegion( region ); // iterators over output and input itk::ImageRegionConstIteratorWithIndex vecit(vecimg, vecimg->GetLargestPossibleRegion()); itk::VariableLengthVector avg(vecimg->GetVectorLength()); avg.Fill(0); float numPixels = 0; while ( ! vecit.IsAtEnd() ) { VecImgType::PointType point; vecimg->TransformIndexToPhysicalPoint(vecit.GetIndex(), point); MaskImgType::IndexType index; maskItk->TransformPhysicalPointToIndex(point, index); if(maskItk->GetPixel(index) != 0) { avg += vecit.Get(); numPixels += 1.0; } // update iterators ++vecit; } avg /= numPixels; m_Controls->m_Warning->setText(QString("Averaging ")+QString::number((int)numPixels)+QString(" voxels!")); m_Controls->m_Warning->setVisible(true); roiImage->CopyInformation( vecimg ); roiImage->SetRegions( region ); roiImage->SetOrigin( pos ); roiImage->Allocate(); roiImage->SetPixel(index, avg); if( m_Controls->m_ModelTabSelectionWidget->currentIndex() ) { FitKurtosis(roiImage, mitk::DiffusionPropertyHelper::GetGradientContainer(diffusionImg), mitk::DiffusionPropertyHelper::GetReferenceBValue(diffusionImg), index); } else { FittIVIM(roiImage, mitk::DiffusionPropertyHelper::GetGradientContainer(diffusionImg), mitk::DiffusionPropertyHelper::GetReferenceBValue(diffusionImg), false, index); } // do not update until selection changed, the values will remain the same as long as the mask is selected! m_HoldUpdate = true; } vecimg->SetRegions( vecimg->GetLargestPossibleRegion() ); } bool QmitkIVIMView::FitKurtosis( itk::VectorImage *vecimg, DirContainerType *dirs, float bval, OutImgType::IndexType &crosspos ) { KurtosisFilterType::Pointer filter = KurtosisFilterType::New(); itk::KurtosisFitConfiguration fit_config; fit_config.omit_bzero = m_Controls->m_OmitBZeroCB->isChecked(); if( m_Controls->m_UseKurtosisBoundsCB->isChecked() ) { fit_config.use_K_limits = true; vnl_vector_fixed k_limits; k_limits[0] = m_Controls->m_KurtosisRangeWidget->minimumValue(); k_limits[1] = m_Controls->m_KurtosisRangeWidget->maximumValue(); fit_config.K_limits = k_limits; } fit_config.fit_scale = static_cast(m_Controls->m_KurtosisFitScale->currentIndex() ); m_KurtosisSnap = filter->GetSnapshot( vecimg->GetPixel( crosspos ), dirs, bval, fit_config ); QString param_label_text("K=%2, D=%1"); param_label_text = param_label_text.arg( m_KurtosisSnap.m_K, 4); param_label_text = param_label_text.arg( m_KurtosisSnap.m_D, 4); m_Controls->m_FittedParamsLabel->setText(param_label_text); const double maxb = m_KurtosisSnap.bvalues.max_value(); double S0 = m_KurtosisSnap.m_Bzero; if (m_KurtosisSnap.m_fittedBZero) S0 = m_KurtosisSnap.m_BzeroFit; std::vector< std::pair > d_line; d_line.emplace_back(0, S0); d_line.emplace_back(maxb, S0*exp(-maxb * m_KurtosisSnap.m_D)); m_Controls->m_ChartWidget->UpdateData2D(d_line, "D-part of fitted model"); const unsigned int num_samples = 50; std::vector< std::pair > y; for( unsigned int i=0; i<=num_samples; ++i) { double b = (((1.0)*i)/(1.0*num_samples))*maxb; y.emplace_back(b, S0 * exp( -b * m_KurtosisSnap.m_D + b*b * m_KurtosisSnap.m_D * m_KurtosisSnap.m_D * m_KurtosisSnap.m_K / 6.0 )); } m_Controls->m_ChartWidget->UpdateData2D(y, "fitted model"); std::vector< std::pair > y_meas; for (unsigned int i=0; im_OmitBZeroCB->isChecked() || m_KurtosisSnap.bvalues[i] > 0.01) y_meas.emplace_back(m_KurtosisSnap.bvalues[i], m_KurtosisSnap.measurements[i]); } m_Controls->m_ChartWidget->UpdateData2D(y_meas, "signal values"); return true; } bool QmitkIVIMView::FittIVIM(itk::VectorImage* vecimg, DirContainerType* dirs, float bval, bool multivoxel, OutImgType::IndexType &crosspos) { IVIMFilterType::Pointer filter = IVIMFilterType::New(); filter->SetInput(vecimg); filter->SetGradientDirections(dirs); filter->SetBValue(bval); switch(m_Controls->m_MethodCombo->currentIndex()) { case 0: filter->SetMethod(IVIMFilterType::IVIM_FIT_ALL); filter->SetS0Thres(m_Controls->m_S0ThreshLabel->text().toDouble()); break; case 1: filter->SetMethod(IVIMFilterType::IVIM_DSTAR_FIX); filter->SetDStar(m_Controls->m_DStarLabel->text().toDouble()); filter->SetS0Thres(m_Controls->m_S0ThreshLabel->text().toDouble()); break; case 2: filter->SetMethod(IVIMFilterType::IVIM_D_THEN_DSTAR); filter->SetBThres(m_Controls->m_BThreshLabel->text().toDouble()); filter->SetS0Thres(m_Controls->m_S0ThreshLabel->text().toDouble()); filter->SetFitDStar(m_Controls->m_CheckDStar->isChecked()); break; case 3: filter->SetMethod(IVIMFilterType::IVIM_LINEAR_D_THEN_F); filter->SetBThres(m_Controls->m_BThreshLabel->text().toDouble()); filter->SetS0Thres(m_Controls->m_S0ThreshLabel->text().toDouble()); filter->SetFitDStar(m_Controls->m_CheckDStar->isChecked()); break; case 4: filter->SetMethod(IVIMFilterType::IVIM_REGULARIZED); filter->SetBThres(m_Controls->m_BThreshLabel->text().toDouble()); filter->SetS0Thres(m_Controls->m_S0ThreshLabel->text().toDouble()); filter->SetNumberIterations(m_Controls->m_NumItsLabel->text().toInt()); filter->SetLambda(m_Controls->m_LambdaLabel->text().toDouble()); filter->SetFitDStar(m_Controls->m_CheckDStar->isChecked()); break; } if(!multivoxel) { filter->SetFitDStar(true); } filter->SetNumberOfThreads(1); filter->SetVerbose(false); filter->SetCrossPosition(crosspos); try{ filter->Update(); m_IvimSnap = filter->GetSnapshot(); m_DStarMap = filter->GetOutput(2); m_DMap = filter->GetOutput(1); m_fMap = filter->GetOutput(); QString param_label_text("f=%1, D=%2, D*=%3"); param_label_text = param_label_text.arg(m_IvimSnap.currentF,4); param_label_text = param_label_text.arg(m_IvimSnap.currentD,4); param_label_text = param_label_text.arg(m_IvimSnap.currentDStar,4); m_Controls->m_FittedParamsLabel->setText(param_label_text); double maxb = m_IvimSnap.bvalues.max_value(); std::vector< std::pair > d_line; d_line.emplace_back(0, 1-m_IvimSnap.currentFunceiled); d_line.emplace_back(maxb, d_line[0].second*exp(-maxb * m_IvimSnap.currentD)); m_Controls->m_ChartWidget->UpdateData2D(d_line, "D-part of fitted model"); std::vector< std::pair > y; int nsampling = 50; double f = 1-m_IvimSnap.currentFunceiled; for(int i=0; i<=nsampling; i++) { double x = (((1.0)*i)/(1.0*nsampling))*maxb; y.emplace_back(x, f*exp(- x * m_IvimSnap.currentD) + (1-f)*exp(- x * (m_IvimSnap.currentD+m_IvimSnap.currentDStar))); } m_Controls->m_ChartWidget->UpdateData2D(y, "fitted model"); std::vector< std::pair > y_meas; for (unsigned int i=0; im_ChartWidget->UpdateData2D(y_meas, "signal values"); if(m_IvimSnap.num_high) // num_high is 0 if there is only one fit { AddSecondFitPlot(); std::vector< std::pair > additonal_meas; for (int i=0; im_ChartWidget->UpdateData2D(additonal_meas, "signal values (used for second fit)"); } else { RemoveSecondFitPlot(); } } catch (itk::ExceptionObject &ex) { MITK_INFO << ex ; m_Controls->m_Warning->setText(QString("IVIM fit not possible: ")+ex.GetDescription()); m_Controls->m_Warning->setVisible(true); return false; } return true; } void QmitkIVIMView::OutputToDatastorage(mitk::DataNode::Pointer node) { mitk::LookupTable::Pointer lut = mitk::LookupTable::New(); lut->SetType( mitk::LookupTable::JET ); mitk::LookupTableProperty::Pointer lut_prop = mitk::LookupTableProperty::New(); lut_prop->SetLookupTable( lut ); if(m_Controls->m_CheckDStar->isChecked()) { mitk::Image::Pointer dstarimage = mitk::Image::New(); dstarimage->InitializeByItk(m_DStarMap.GetPointer()); dstarimage->SetVolume(m_DStarMap->GetBufferPointer()); - QString newname2 = ""; newname2 = newname2.append("IVIM_DStarMap_Method-%1").arg(m_Controls->m_MethodCombo->currentText()); + QString newname2 = ""; newname2 = newname2.append("D* Fit-%1").arg(m_Controls->m_MethodCombo->currentIndex()+1); mitk::DataNode::Pointer node2=mitk::DataNode::New(); node2->SetData( dstarimage ); node2->SetName(newname2.toLatin1()); node2->SetProperty("LookupTable", lut_prop ); + mitk::LevelWindowProperty::Pointer levWinProp = mitk::LevelWindowProperty::New(); + mitk::LevelWindow levelwindow; + levelwindow.SetRangeMinMax(0, 0.2); + levWinProp->SetLevelWindow(levelwindow); + node2->SetProperty("levelwindow", levWinProp); + GetDataStorage()->Add(node2, node); } if(m_Controls->m_CheckD->isChecked()) { mitk::Image::Pointer dimage = mitk::Image::New(); dimage->InitializeByItk(m_DMap.GetPointer()); dimage->SetVolume(m_DMap->GetBufferPointer()); - QString newname1 = ""; newname1 = newname1.append("IVIM_DMap_Method-%1").arg(m_Controls->m_MethodCombo->currentText()); + QString newname1 = ""; newname1 = newname1.append("D Fit-%1").arg(m_Controls->m_MethodCombo->currentIndex()+1); mitk::DataNode::Pointer node1=mitk::DataNode::New(); node1->SetData( dimage ); node1->SetName(newname1.toLatin1()); node1->SetProperty("LookupTable", lut_prop ); + mitk::LevelWindowProperty::Pointer levWinProp = mitk::LevelWindowProperty::New(); + mitk::LevelWindow levelwindow; + levelwindow.SetRangeMinMax(0, 0.003); + levWinProp->SetLevelWindow(levelwindow); + node1->SetProperty("levelwindow", levWinProp); + GetDataStorage()->Add(node1, node); } if(m_Controls->m_Checkf->isChecked()) { mitk::Image::Pointer image = mitk::Image::New(); image->InitializeByItk(m_fMap.GetPointer()); image->SetVolume(m_fMap->GetBufferPointer()); - QString newname0 = ""; newname0 = newname0.append("IVIM_fMap_Method-%1").arg(m_Controls->m_MethodCombo->currentText()); + QString newname0 = ""; newname0 = newname0.append("f Fit-%1").arg(m_Controls->m_MethodCombo->currentIndex()+1); mitk::DataNode::Pointer node3=mitk::DataNode::New(); node3->SetData( image ); node3->SetName(newname0.toLatin1()); node3->SetProperty("LookupTable", lut_prop ); + mitk::LevelWindowProperty::Pointer levWinProp = mitk::LevelWindowProperty::New(); + mitk::LevelWindow levelwindow; + levelwindow.SetRangeMinMax(0, 1); + levWinProp->SetLevelWindow(levelwindow); + node3->SetProperty("levelwindow", levWinProp); + GetDataStorage()->Add(node3, node); } this->GetRenderWindowPart()->RequestUpdate(); } void QmitkIVIMView::ClipboardCurveButtonClicked() { // Kurtosis if ( m_Controls->m_ModelTabSelectionWidget->currentIndex() ) { std::stringstream ss; QString clipboard("Measurement Points\n"); ss << m_KurtosisSnap.bvalues << "\n" << m_KurtosisSnap.measurements << "\n\n"; ss << "Fitted Values ( D K [b_0] ) \n" << m_KurtosisSnap.m_D << " " << m_KurtosisSnap.m_K; if( m_KurtosisSnap.m_fittedBZero ) ss << " " << m_KurtosisSnap.m_BzeroFit; ss << "\n\n"; clipboard.append( QString( ss.str().c_str() )); ss.str( std::string() ); ss.clear(); QApplication::clipboard()->setText(clipboard, QClipboard::Clipboard ); } else { std::stringstream ss; QString clipboard("Normalized Measurement Points\n"); ss << m_IvimSnap.bvalues << "\n" << m_IvimSnap.allmeas << "\n\n"; ss << "Fitted Values ( f D D* ) \n" << m_IvimSnap.currentF << " " << m_IvimSnap.currentD << " " << m_IvimSnap.currentDStar; ss << "\n\n"; clipboard.append( QString( ss.str().c_str() )); ss.str( std::string() ); ss.clear(); QApplication::clipboard()->setText(clipboard, QClipboard::Clipboard ); } } void QmitkIVIMView::SavePlotButtonClicked() { m_Controls->m_ChartWidget->SavePlotAsImage(); } void QmitkIVIMView::ClipboardStatisticsButtonClicked() { // Kurtosis if ( m_Controls->m_ModelTabSelectionWidget->currentIndex() ) { QString clipboard( "D \t K \n" ); clipboard = clipboard.append( "%L1 \t %L2" ) .arg( m_KurtosisSnap.m_D, 0, 'f', 10 ) .arg( m_KurtosisSnap.m_K, 0, 'f', 10 ) ; QApplication::clipboard()->setText(clipboard, QClipboard::Clipboard ); } else { QString clipboard( "f \t D \t D* \n" ); clipboard = clipboard.append( "%L1 \t %L2 \t %L3" ) .arg( m_IvimSnap.currentF, 0, 'f', 10 ) .arg( m_IvimSnap.currentD, 0, 'f', 10 ) .arg( m_IvimSnap.currentDStar, 0, 'f', 10 ) ; QApplication::clipboard()->setText(clipboard, QClipboard::Clipboard ); } } void QmitkIVIMView::Activated() { m_Active = true; } void QmitkIVIMView::Deactivated() { m_Active = false; } void QmitkIVIMView::Visible() { m_Visible = true; if (this->GetRenderWindowPart() && !m_ListenerActive) { m_SliceChangeListener.RenderWindowPartActivated(this->GetRenderWindowPart()); connect(&m_SliceChangeListener, SIGNAL(SliceChanged()), this, SLOT(OnSliceChanged())); m_ListenerActive = true; } } void QmitkIVIMView::Hidden() { m_Visible = false; } diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/src/internal/QmitkIVIMViewControls.ui b/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/src/internal/QmitkIVIMViewControls.ui index 9d36f29..175f5c7 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/src/internal/QmitkIVIMViewControls.ui +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.ivim/src/internal/QmitkIVIMViewControls.ui @@ -1,1213 +1,1213 @@ QmitkIVIMViewControls 0 0 424 689 0 0 QmitkTemplate QCommandLinkButton:disabled { border: none; } QGroupBox { background-color: transparent; } 9 9 9 9 9 QFrame::NoFrame QFrame::Raised 0 0 0 0 warning display Qt::RichText true 0 0 16 16 QFrame::NoFrame QFrame::Raised 0 0 0 0 0 0 16 16 QFrame::NoFrame QFrame::Raised 0 0 0 0 0 0 16 16 QFrame::NoFrame QFrame::Raised 0 0 0 0 0 0 0 0 16777215 16777215 Generate Parameter Maps QFrame::StyledPanel QFrame::Raised 0 0 0 0 0 0 0 IVIM Parameters 9 QFrame::NoFrame QFrame::Raised 0 0 0 0 0 51 16777215 200 Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter 80 16777215 D* 100 60 Qt::Horizontal QFrame::NoFrame QFrame::Plain 0 0 0 0 0 130 0 130 16777215 Signal threshold: 100 0 Qt::Horizontal 0 0 QFrame::NoFrame QFrame::Raised 0 0 0 0 0 0 0 30 16777215 TextLabel Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter 15 16777215 Calculate threshold from histogram * QFrame::NoFrame QFrame::Raised 0 0 0 0 0 130 0 130 16777215 Ignore b< (first fit) 250 34 Qt::Horizontal 51 16777215 46.5 Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter QFrame::NoFrame QFrame::Raised 0 0 0 0 0 80 16777215 #iterations: 100 10 Qt::Horizontal 30 16777215 TextLabel Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter QFrame::NoFrame QFrame::Raised 0 0 0 0 0 80 16777215 lambda: 1000 10 Qt::Horizontal 0 0 QFrame::NoFrame QFrame::Raised 0 0 0 0 0 0 0 30 16777215 TextLabel Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter 15 16777215 Calculate threshold from histogram * QFrame::NoFrame QFrame::Raised 0 0 0 0 Fit method: 0 - Jointly fit D, f and D* + 1. Jointly fit D, f and D* - Fit D & f with fixed D* value + 2. Fit D & f with fixed D* value - Fit D & f (high b), then fit D* + 3. Fit D & f (high b), then fit D* - Linearly fit D & f (high b), then fit D* + 4. Linearly fit D & f (high b), then fit D* QFrame::NoFrame QFrame::Raised 0 0 0 0 80 0 Output Maps f true D false D* false Kurtosis QFrame::StyledPanel QFrame::Raised 2 2 2 2 2 Smoothing sigma Select Fit Type Omit b=0 Measurements false 80 0 Output Maps Force the fitting of K to remain within the given boundaries Boundaries for K Select if the data is fitted directly (straight) or the logarithmic equation is used Straight Fit Logarithmic Fit 2 QLayout::SetMaximumSize D false K true Signa for gaussian smoothing applied prior to map computation 0.000000000000000 5.000000000000000 0.100000000000000 On Input Data 6 6 6 6 Optional ROI image ROI: DWI to analyze Raw DWI: Qt::Vertical 20 40 Copy the signal values in the current voxel to the clipboard. Save plot as image QFrame::NoFrame QFrame::Raised 0 0 0 0 QFrame::NoFrame QFrame::Raised 0 0 0 0 Copy the signal values in the current voxel to the clipboard. Signal values to clipboard Copy the parameters of the curve fit in the current voxel to the clipboard. Fit-parameters to clipboard Intra Voxel Incoherent Motion Estimation 6 9 6 6 Qt::AlignCenter QmitkDataStorageComboBox QComboBox
QmitkDataStorageComboBox.h
 QmitkDataStorageComboBoxWithSelectNone QComboBox
QmitkDataStorageComboBoxWithSelectNone.h
 ctkRangeWidget QWidget
ctkRangeWidget.h
1 QmitkChartWidget QWidget
QmitkChartWidget.h
1 diff --git a/README.md b/README.md index 188d223..8cd8faa 100644 --- a/README.md +++ b/README.md @@ -1,244 +1,255 @@ MITK Diffusion ============== Copyright © German Cancer Research Center (DKFZ), [Division of Medical Image Computing (MIC)](https://www.dkfz.de/en/mic/index.php). Please make sure that your usage of this code is in compliance with the code [license](https://github.com/MIC-DKFZ/MITK-Diffusion/blob/master/LICENSE.txt). [![DOI](https://zenodo.org/badge/195387051.svg)](https://zenodo.org/badge/latestdoi/195387051) The MITK Diffusion application [[1,2]](#References) offers a selection of image analysis algorithms for the processing of diffusion-weighted MR images. It encompasses the research of the Division of Medical Image Computing at the German Cancer Research Center (DKFZ). * [Downloads](#Downloads) * [Requirements](#Requirements) * [Features](#Features) * [Related Links](#Related-Links) * [Image Gallery](#Image-Gallery) * [Building MITK Diffusion from source](#Building-MITK-Diffusion-from-source) -* [User Manual](http://docs.mitk.org/nightly/org_mitk_gui_qt_diffusionimaging.html) +* [User Manual](http://docs.mitk.org/diffusion/nightly/) * [Report a Bug](https://phabricator.mitk.org/maniphest/task/edit/form/29/) * [References](#References) * [Contact](#Contact) ## Downloads -**Nightly Ubuntu and Windows installers: [ftp://ftp.dkfz-heidelberg.de/outgoing/MitkDiffusion](https://bit.ly/2S1QfC8)** +Please have a look at the [requirements](#Requirements) for running MITK Diffusion with all its features successfully! -Please also have a look at the [requirements](#Requirements) for running MITK Diffusion with all its features successfully! +The nightly installers come as executable setup wizards that install MITK Diffusion on your system or alternatively as simple .tar.gz or .zip archive where you can execute MITK Diffusion and the command line apps "manually". Should there be no new installer for a while, please [contact](#Contact) us and report the issue. + +**Windows 64-bit**: +* [Windows 10 executable (with Python support)](http://www.mitk.org/download/diffusion/nightly/MITK-Diffusion_Windows-10_Python.exe.html) +* [Windows 10 zip (with Python support)](http://www.mitk.org/download/diffusion/nightly/MITK-Diffusion_Windows-10_Python.zip.html) +* [Windows 10 executable (without Python support)](http://www.mitk.org/download/diffusion/nightly/MITK-Diffusion_Windows-10_NoPython.exe.html) +* [Windows 10 zip (without Python support)](http://www.mitk.org/download/diffusion/nightly/MITK-Diffusion_Windows-10_NoPython.zip.html) + +**Linux 64-bit**: +* [Ubuntu 18.04 executable (with Python support)](http://www.mitk.org/download/diffusion/nightly/MITK-Diffusion_ubuntu-18.04_Python.run.html) +* [Ubuntu 18.04 tarball (with Python support)](http://www.mitk.org/download/diffusion/nightly/MITK-Diffusion_ubuntu-18.04_Python.tar.gz.html) +* [Ubuntu 18.04 executable (without Python support)](http://www.mitk.org/download/diffusion/nightly/MITK-Diffusion_ubuntu-18.04_NoPython.run.html) +* [Ubuntu 18.04 tarball (without Python support)](http://www.mitk.org/download/diffusion/nightly/MITK-Diffusion_ubuntu-18.04_NoPython.tar.gz.html) -The installers come as executable setup wizards that install MITK Diffusion on your system or alternatively as simple .tar.gz or .zip archive where you can execute MITK Diffusion and the command line apps "manually". Should there be no new installer for a while, please [contact](#Contact) us and report the issue. If you encounter any bugs, please report them in our [bugtracking](https://phabricator.mitk.org/maniphest/task/edit/form/29/) system or use the [MITK-users mailing list](http://mitk.org/wiki/MITK_Mailinglist). We are grateful for any feedback! ### Requirements * Install Python 3.X: `sudo apt install python3 python3-pip` (Ubuntu) or from https://www.python.org/downloads/windows/ (Windows) * Download our Python requirements file: [PythonRequirements.txt](https://github.com/MIC-DKFZ/MITK-Diffusion/tree/master/PythonRequirements.txt) * Install the Python requirements: `pip3 install -r PythonRequirements.txt` * If your are behind a proxy use `pip3 --proxy install -r PythonRequirements.txt` **For Windows users**: MITK Diffusion requires the Microsoft Visual C++ 2017 Redistributable to be installed on the system. The MITK Diffusion installer automatically installs this redistributable for you if not already present on the system, but it needs administrative privileges to do so. So to install the redistributable, **run the MITK Diffusion installer as administrator**. ## Features **Support for most established image formats** * Images: DICOM, NIFTI, NRRD (peak and SH images compatible with MRtrix) * Tractograms: fib/vtk, tck and trk. **Image preprocessing** * Registration * Head-motion correction * Denoising * Skull stripping and brain mask segmentation (Linux only) * Resampling, cropping, flipping and merging * Header modifications * Single volume extraction **Diffusion gradient/b-value processing** * b-value rounding * Gradient direction flipping * Gradient direction subsampling * Averaging of gradient directions/volumes * Gradient direction and b-value visualization **ODF reconstruction and signal modelling** * Tensor and Q-ball reconstruction * Other reconstructions via Dipy wrapping (CSD, 3D SHORE, SFM) (Linux only) * ODF peak calculation * MRtrix or camino results can be imported **Quantification of diffusion-weighted/tensor/ODF images** * Intravoxel Incoherent Motion (IVIM) and diffusion kurtosis analysis * Calculation of many other derived indices such as ADC, MD, GFA, FA, RA, AD, RD * Image statistics **Segmentation** * Automatic white matter bundle segmentation (TractSeg) [[3]](#References) (Linux only) * Automatic brain mask segmentation (Linux only) * Manual image segmentation and operations on segmentations * SOON: automatic brain tissue segmentation **Fiber tractography** * Global tractography [[4]](#References) * Streamline tractography * Interactive (similar to [[5]](#References)) or seed image based * Deterministic or probabilistic * Peak, ODF, tensor and raw dMRI based. The latter one in conjunction with machine learning based tractography [[6]](#References) * Various possibilities for anatomical constraints. * Tractography priors in form of additional peak images, e.g. obtained using TractSeg **Fiber processing** * Tract dissection (parcellation or ROI based) * Tract filtering by * length * curvature * direction * weight * density * Tract resampling and compression * Tract transformation * Mirroring * Rotating and translating * Registration (apply transform of previously performed image registration) * Tract coloring * Curvature * Length * Weight * Scalar map (e.g. FA) * Other operations * Join * Subtract * Copy * Fiber clustering [[7]](#References) * Fiber fitting and weighting similar to SIFT2 and LiFE [[8,9]](#References) * Principal direction extraction (fibers --> peaks) * Tract derived images: * Tract density images * Tract endpoint images * Tract envelopes **Fiberfox dMRI simulations** [[10]](#References) * Multi-compartment signal modeling * Simulation of the k-space acquisition including * Compartment specific relaxation effects * Artifacts such as noise, spikes, ghosts, aliasing, distortions, signal drift, head motion, eddy currents and Gibbs ringing * Definition of important acquisition parameters such as bvalues and gradient directions, TE, TR, dwell time, partial Fourier, ... * Manual definition of fiber configurations, e.g. for evaluation purposes * Automatic generation of random fiber configurations **Other features** * Brain network statistics and visualization (connectomics) * Interactive Python console (Linux only) * Integrated screenshot maker * Command line tools for most functionalities ## Related Links * Great python package for logging your (MITK) command line experiments: * https://github.com/MIC-DKFZ/cmdint * `pip3 install cmdint` * TractSeg reference data of 72 semiautomatically defined bundles in 105 HCP subjects: https://zenodo.org/record/1285152 * TractSeg python package: https://github.com/MIC-DKFZ/TractSeg * Simulated dMRI images and ground truth of random fiber phantoms in various configurations: https://doi.org/10.5281/zenodo.2533250 * ISMRM 2015 Tractography Challenge Data: https://doi.org/10.5281/zenodo.572345 & https://doi.org/10.5281/zenodo.1007149 ## Image Gallery ![](http://mitk.org/images/8/8f/MitkDiffusion.png) Screenshot of the MITK Diffusion Welcome Screen


![](http://mitk.org/images/0/09/ScalarMaps.png) Scalar map visualization


![](http://mitk.org/images/3/3b/Data_Tensors.png) Tensor Visualization


![](http://mitk.org/images/5/5c/Data_ODF.png) ODF visualization


![](http://mitk.org/images/7/73/Data_Peaks.png) Peak visualization (uniform white coloring)


![](http://mitk.org/images/6/68/StreamlineTractography.png) Interactive tractography in MITK Diffusion. The tractogram updates automatically on parameter change and movement of the spherical seed region.


![](http://mitk.org/images/3/3a/Extraction_1.png) Tract dissection using manually drawn ROIs.


![](http://mitk.org/images/f/f5/FiberFit.png) Automatic streamline weighting (similar to SIFT2 or LiFE)


![](http://mitk.org/images/3/33/Fiberfox.png) Illustration of the dMRI phantom simulation process using Fiberfox.


\ Illustration of simulated dMRI images with various artifacts (a bit excessive for illustration purposes): eddy current distortions (1), motion and spike (2), intensity drift (3), motion, eddy and noise (4), ringing (5), B0 inhomogeneity distortions (6), from left to right.


![](http://mitk.org/images/0/08/RandomFibers_Example.png) Automatically generated random fiber configuration for Fiberfox simulations.


## Building MITK Diffusion from source * Install [Qt](https://www.qt.io/) on your system (>= 5.12.6). * Clone MITK from [github](https://github.com/MIC-DKFZ/MITK-Diffusion.git) using [Git version control](https://git-scm.com/). * Clone MITK Diffusion from [github](https://github.com/MITK/MITK.git). * Configure the MITK Superbuild using [CMake](https://cmake.org/) (>= 3.14.5). * Choose the MITK source code directory and an empty binary directory. * Click "Configure". * Set the option MITK_EXTENSION_DIRS to "/path/to/my/mitk-diffusion-repository". * Click "Configure". * Set the option MITK_BUILD_CONFIGURATION to "DiffusionRelease". * Click "Generate". * macOS specifics (unclear if this still applies): * Use python 3.**6**, since python 3.**7** leads to build errors on macOS. * The cmake variables for python 3 might need to be set manually. It is probably enough to specify PYTHON_EXECUTABLE. * Openmp needs to be installed manually since it is not included in apple clang anymore: "brew install libomp" should do the trick. It might be necessary to set the corresponding make variables later in the MITK build manually: * OpenMP_CXX_FLAGS: -Xpreprocessor -fopenmp -I"/path/to/python3/includes/" * OpenMP_C_FLAGS: -Xpreprocessor -fopenmp -I"/path/to/python3/includes/" * OpenMPCXX_LIB_NAMES: libomp * OpenMPC_LIB_NAMES: libomp * OpenMP_libomp_LIBRARY: /path/to/libomp.dylib * Start the Superbuild: * Linux/maxOS: Open a console window, navigate to the build folder and type "make -j8" (optionally supply the number threads to be used for a parallel build with -j). * Windows (requires visual studio): Open the MITK Superbuild solution file and build all projects. * The Superbuild may take some time. * After the Superbuild has finished, change the cmake binary directory from "/path/to/my/build" to "/path/to/my/build/MITK-build" and configure+generate again. * Build again in "/path/to/my/build/MITK-build" using make (linux/mac) or the VS solution file. * The build may again take some time and should yield the binaries in "/path/to/my/build/MITK-build/bin" More detailed build instructions can be found in the [documentation](http://docs.mitk.org/nightly/BuildInstructionsPage.html). Continuous integration: http://cdash.mitk.org/index.php?project=MITK&display=project ## References All publications of the Division of Medical Image Computing can be found [https://www.dkfz.de/en/mic/publications/ here]. [1] Fritzsche, Klaus H., Peter F. Neher, Ignaz Reicht, Thomas van Bruggen, Caspar Goch, Marco Reisert, Marco Nolden, et al. “MITK Diffusion Imaging.” Methods of Information in Medicine 51, no. 5 (2012): 441. [2] Fritzsche, K., and H.-P. Meinzer. “MITK-DI A New Diffusion Imaging Component for MITK.” In Bildverarbeitung Für Die Medizin, n.d. [3] Wasserthal, Jakob, Peter Neher, and Klaus H. Maier-Hein. “TractSeg - Fast and Accurate White Matter Tract Segmentation.” NeuroImage 183 (August 4, 2018): 239–53. [4] Neher, P. F., B. Stieltjes, M. Reisert, I. Reicht, H.P. Meinzer, and K. Maier-Hein. “MITK Global Tractography.” In SPIE Medical Imaging: Image Processing, 2012. [5] Chamberland, M., K. Whittingstall, D. Fortin, D. Mathieu, und M. Descoteaux. „Real-time multi-peak tractography for instantaneous connectivity display“. Front Neuroinform 8 (2014): 59. doi:10.3389/fninf.2014.00059. [6] Neher, Peter F., Marc-Alexandre Côté, Jean-Christophe Houde, Maxime Descoteaux, and Klaus H. Maier-Hein. “Fiber Tractography Using Machine Learning.” NeuroImage. Accessed July 17, 2017. doi:10.1016/j.neuroimage.2017.07.028. [7] Garyfallidis, Eleftherios, Matthew Brett, Marta Morgado Correia, Guy B. Williams, and Ian Nimmo-Smith. “QuickBundles, a Method for Tractography Simplification.” Frontiers in Neuroscience 6 (2012). [8] Smith, Robert E., Jacques-Donald Tournier, Fernando Calamante, and Alan Connelly. “SIFT2: Enabling Dense Quantitative Assessment of Brain White Matter Connectivity Using Streamlines Tractography.” NeuroImage 119, no. Supplement C (October 1, 2015): 338–51. [9] Pestilli, Franco, Jason D. Yeatman, Ariel Rokem, Kendrick N. Kay, and Brian A. Wandell. “Evaluation and Statistical Inference for Human Connectomes.” Nature Methods 11, no. 10 (October 2014): 1058–63. [10] Neher, Peter F., Frederik B. Laun, Bram Stieltjes, and Klaus H. Maier-Hein. “Fiberfox: Facilitating the Creation of Realistic White Matter Software Phantoms.” Magnetic Resonance in Medicine 72, no. 5 (November 2014): 1460–70. doi:10.1002/mrm.25045. ## Contact If you have questions about the application or if you would like to give us feedback, don't hesitate to contact us using [our mailing list](http://mitk.org/wiki/MITK_Mailinglist) or, for questions that are of no interest for the community, [directly](https://www.dkfz.de/en/mic/team/people/Peter_Neher.html).