diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.python/resources/dipy_reconstructions.py b/Plugins/org.mitk.gui.qt.diffusionimaging.python/resources/dipy_reconstructions.py index 922c449441..a567d4c651 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.python/resources/dipy_reconstructions.py +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.python/resources/dipy_reconstructions.py @@ -1,391 +1,410 @@ +import sys + + +def get_mitk_sphere(): + """ Return MITK compliant dipy Sphere object. + MITK stores ODFs as 252 values spherically sampled from the continuous ODF. + The sampling directions are generate by a 5-fold subdivisions of an icosahedron. + """ + xyz = np.array([ + 0.9756767549555488, 0.9977154378498742, 0.9738192119472443, + 0.8915721200771204, 0.7646073555341725, 0.6231965669156312, + 0.9817040172417226, 0.9870396762453547, 0.9325589150767597, + 0.8173592116492303, 0.6708930871960926, 0.9399233672993689, + 0.9144882783890762, 0.8267930935417315, 0.6931818659696647, + 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-0.8573751662344773, -0.2980738893651726, -0.3916343988495664, + -0.4596955428592778, -0.4950341577852201, -0.1432117197792371, + -0.2267418620329016, -0.2909964852939082, 0.02097514873862574, + -0.05800679989935065, 0.1653145532988453, -0.3786231842883476, + -0.1464197032303796, 0.09531724619007391, -0.1924163631703616, + 0.05252803743712917, 0.006318730357784829, -0.3534800054422614, + -0.1720548071373146, 0.02057294660420643, 0.190134278339324, + -0.1169519894866824, 0.07636807502743861, 0.2529338262925594, + 0.1271908635410245, 0.3046134343217798, 0.3366066958443542, + 0.6094980941008995, 0.7135382519498201, 0.7711196978950583, + 0.7870198804193677, 0.8705500304441893, 0.9132984713369965, + 0.403998910419839, 0.62060207699311, 0.7967976318501995, + 0.4726965405256068, 0.6757048258462731, 0.5106167801856609]) + + n = int(xyz.shape[0] / 3) + x = xyz[:n] + y = xyz[n:2 * n] + z = xyz[2 * n:] + + for i in range(n): + v = np.array([x[i], y[i], z[i]]) + norm = np.linalg.norm(v) + if norm > 0: + v /= norm + x[i] = v[0] + y[i] = v[1] + z[i] = v[2] + + s = sphere.Sphere(x=x, y=y, z=z) + return s + + error_string = None del error_string try: import dipy.direction.peaks as dpp from dipy.reconst.shore import ShoreModel from dipy.reconst.shm import CsaOdfModel, OpdtModel, SphHarmModel import dipy.reconst.sfm as sfm from dipy.reconst.csdeconv import auto_response, ConstrainedSphericalDeconvModel from dipy.core import sphere import numpy as np from dipy.core.gradients import gradient_table import SimpleITK as sitk + print('DIPY Reconstructions') + data = sitk.GetArrayFromImage(in_image) + bvals = np.array(bvals) + bvecs = np.array(bvecs) - def get_mitk_sphere(): - """ Return MITK compliant dipy Sphere object. - MITK stores ODFs as 252 values spherically sampled from the continuous ODF. - The sampling directions are generate by a 5-fold subdivisions of an icosahedron. - """ - xyz = np.array([ - 0.9756767549555488, 0.9977154378498742, 0.9738192119472443, - 0.8915721200771204, 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0.6757048258462731, 0.5106167801856609]) - - n = int(xyz.shape[0] / 3) - x = xyz[:n] - y = xyz[n:2 * n] - z = xyz[2 * n:] - - for i in range(n): - v = np.array([x[i], y[i], z[i]]) - norm = np.linalg.norm(v) - if norm > 0: - v /= norm - x[i] = v[0] - y[i] = v[1] - z[i] = v[2] - - s = sphere.Sphere(x=x, y=y, z=z) - return s + # create dipy Sphere + sphere = get_mitk_sphere() + odf = None + model = None + gtab = gradient_table(bvals, bvecs) + if mask is not None: + mask = sitk.GetArrayFromImage(mask) + print(mask.shape) - def fit_data(data, bvals, bvecs, model_type='3D-SHORE', calculate_peak_image=False): - """ Fits the defined model to the input dMRI and returns an MITK compliant ODF image. - """ - # create dipy Sphere - sphere = get_mitk_sphere() - odf = None - model = None - gtab = gradient_table(bvals, bvecs) + # fit selected model + if model_type == '3D-SHORE': + print('Fitting 3D-SHORE') + print("radial_order: ", radial_order) + print("zeta: ", zeta) + print("lambdaN: ", lambdaN) + print("lambdaL: ", lambdaL) + model = ShoreModel(gtab, radial_order=radial_order, zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL) + asmfit = model.fit(data) + odf = asmfit.odf(sphere) + elif model_type == 'CSA-QBALL': + print('Fitting CSA-QBALL') + print("sh_order: ", sh_order) + print("smooth: ", smooth) + model = CsaOdfModel(gtab=gtab, sh_order=sh_order, smooth=smooth) + odf = model.fit(data, mask=mask).odf(sphere) + odf = np.clip(odf, 0, np.max(odf, -1)[..., None]) + elif model_type == 'SFM': + print('Fitting SFM') + print("fa_thr: ", fa_thr) + response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=fa_thr) + model = sfm.SparseFascicleModel(gtab, sphere=sphere, + l1_ratio=0.5, alpha=0.001, + response=response[0]) + odf = model.fit(data, mask=mask).odf(sphere) + elif model_type == 'CSD': + print('Fitting CSD') + print("sh_order: ", sh_order) + print("fa_thr: ", fa_thr) + response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=fa_thr) + model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=sh_order) + odf = model.fit(data).odf(sphere) + elif model_type == 'Opdt': + print('Orientation Probability Density Transform') + print("sh_order: ", sh_order) + print("smooth: ", smooth) + model = OpdtModel(gtab=gtab, sh_order=sh_order, smooth=smooth) + odf = model.fit(data, mask=mask).odf(sphere) + else: + raise ValueError('Model type not supported. Available models: 3D-SHORE, CSA-QBALL, SFM, CSD, Opdt') - # fit selected model - if model_type == '3D-SHORE': - print('Fitting 3D-SHORE') - print("radial_order: ", radial_order) - print("zeta: ", zeta) - print("lambdaN: ", lambdaN) - print("lambdaL: ", lambdaL) - model = ShoreModel(gtab, radial_order=radial_order, zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL) - asmfit = model.fit(data) - odf = asmfit.odf(sphere) - elif model_type == 'CSA-QBALL': - print('Fitting CSA-QBALL') - print("sh_order: ", sh_order) - print("smooth: ", smooth) - model = CsaOdfModel(gtab=gtab, sh_order=sh_order, smooth=smooth) - odf = model.fit(data).odf(sphere) - odf = np.clip(odf, 0, np.max(odf, -1)[..., None]) - elif model_type == 'SFM': - print('Fitting SFM') - print("fa_thr: ", fa_thr) - response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=fa_thr) - model = sfm.SparseFascicleModel(gtab, sphere=sphere, - l1_ratio=0.5, alpha=0.001, - response=response[0]) - odf = model.fit(data).odf(sphere) - elif model_type == 'CSD': - print('Fitting CSD') - print("sh_order: ", sh_order) - print("fa_thr: ", fa_thr) - response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=fa_thr) - model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=sh_order) - odf = model.fit(data).odf(sphere) - elif model_type == 'Opdt': - print('Orientation Probability Density Transform') - print("sh_order: ", sh_order) - print("smooth: ", smooth) - model = OpdtModel(gtab=gtab, sh_order=sh_order, smooth=smooth) - odf = model.fit(data).odf(sphere) - else: - raise ValueError('Model type not supported. Available models: 3D-SHORE, CSA-QBALL, SFM, CSD, Opdt') + odf = np.nan_to_num(odf) + print('Preparing ODF image') + odf_image = sitk.Image([data.shape[2], data.shape[1], data.shape[0]], sitk.sitkVectorFloat32, len(sphere.vertices)) + for x in range(data.shape[2]): + for y in range(data.shape[1]): + for z in range(data.shape[0]): + if mask is not None and mask[z, y, x] == 0: + continue + odf_image.SetPixel(x, y, z, odf[z, y, x, :]) - print('Preparing ODF image') - odf_image = sitk.Image([data.shape[2], data.shape[1], data.shape[0]], sitk.sitkVectorFloat32, len(sphere.vertices)) - for x in range(data.shape[2]): - for y in range(data.shape[1]): - for z in range(data.shape[0]): - odf_image.SetPixel(x, y, z, odf[z, y, x, :]) + if num_peaks > 0: - # if not calculate_peak_image: - return odf_image, None + print('Calculating peaks') + sys.stdout.flush() + # calculate peak image + data = np.nan_to_num(data) + sf_peaks = dpp.peaks_from_model(model, + data, + sphere, + relative_peak_threshold=relative_peak_threshold, + min_separation_angle=min_separation_angle, + return_sh=False, npeaks=num_peaks, parallel=True, + mask=mask) - # # calculate peak image - # sf_peaks = dpp.peaks_from_model(model, - # data, - # sphere, - # relative_peak_threshold=.4, - # min_separation_angle=15, - # return_sh=False, npeaks=5, parallel=True) - # - # # reshape to be MITK/MRtrix compliant - # s = sf_peaks.peak_dirs.shape - # peaks = sf_peaks.peak_dirs.reshape((s[0], s[1], s[2], s[3] * s[4]), order='C') - # - # # scale peaks - # for x in range(s[0]): - # for y in range(s[1]): - # for z in range(s[2]): - # for i in range(3): - # peaks[x, y, z, i * 3] *= sf_peaks.peak_values[x, y, z, i] - # peaks[x, y, z, i * 3 + 1] *= sf_peaks.peak_values[x, y, z, i] - # peaks[x, y, z, i * 3 + 2] *= sf_peaks.peak_values[x, y, z, i] - # - # peaks = peaks.astype('float32') - # return odf_image, peaks + # reshape to be MITK/MRtrix compliant + s = sf_peaks.peak_dirs.shape + peaks = sf_peaks.peak_dirs.reshape((s[0], s[1], s[2], num_peaks * 3), order='C') + peaks = np.nan_to_num(peaks) + peak_image = sitk.Image([data.shape[2], data.shape[1], data.shape[0]], sitk.sitkVectorFloat32, num_peaks * 3) + # scale peaks + max_peak = 1.0 + if normalize_peaks: + max_peak = np.max(sf_peaks.peak_values) + if max_peak <= 0: + max_peak = 1.0 + for x in range(s[0]): + for y in range(s[1]): + for z in range(s[2]): + for i in range(num_peaks): + peaks[x, y, z, i * 3] *= sf_peaks.peak_values[x, y, z, i] / max_peak + peaks[x, y, z, i * 3 + 1] *= sf_peaks.peak_values[x, y, z, i] / max_peak + peaks[x, y, z, i * 3 + 2] *= sf_peaks.peak_values[x, y, z, i] / max_peak - print('DIPY Reconstructions') - data = sitk.GetArrayFromImage(in_image) - bvals = np.array(bvals) - bvecs = np.array(bvecs) + peak_image.SetPixel(z, y, x, peaks[x, y, z, :]) - odf_image, peaks = fit_data(data=data, bvals=bvals, bvecs=bvecs, model_type=model_type, calculate_peak_image=calculate_peak_image) + peak_image.SetOrigin(in_image.GetOrigin()) + peak_image.SetSpacing(in_image.GetSpacing()) + peak_image.SetDirection(in_image.GetDirection()) odf_image.SetOrigin(in_image.GetOrigin()) odf_image.SetSpacing(in_image.GetSpacing()) odf_image.SetDirection(in_image.GetDirection()) + except Exception as e: error_string = str(e) print(error_string) + +sys.stdout.flush() diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsView.cpp b/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsView.cpp index 93cacc770b..e77c2f7357 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsView.cpp +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsView.cpp @@ -1,260 +1,319 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ // Blueberry #include #include // Qmitk #include "QmitkDipyReconstructionsView.h" #include #include #include #include #include #include #include #include #include #include +#include +#include +#include +#include +#include +#include const std::string QmitkDipyReconstructionsView::VIEW_ID = "org.mitk.views.dipyreconstruction"; QmitkDipyReconstructionsView::QmitkDipyReconstructionsView() : QmitkAbstractView() , m_Controls( 0 ) { } // Destructor QmitkDipyReconstructionsView::~QmitkDipyReconstructionsView() { } void QmitkDipyReconstructionsView::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::QmitkDipyReconstructionsViewControls; m_Controls->setupUi( parent ); connect( m_Controls->m_ImageBox, SIGNAL(currentIndexChanged(int)), this, SLOT(UpdateGUI()) ); connect( m_Controls->m_StartButton, SIGNAL(clicked()), this, SLOT(StartFit()) ); connect( m_Controls->m_ModelBox, SIGNAL(currentIndexChanged(int)), this, SLOT(UpdateGUI()) ); this->m_Parent = parent; m_Controls->m_ImageBox->SetDataStorage(this->GetDataStorage()); mitk::NodePredicateIsDWI::Pointer isDwi = mitk::NodePredicateIsDWI::New(); m_Controls->m_ImageBox->SetPredicate( isDwi ); + mitk::NodePredicateProperty::Pointer isBinaryPredicate = mitk::NodePredicateProperty::New("binary", mitk::BoolProperty::New(true)); + mitk::TNodePredicateDataType::Pointer isImagePredicate = mitk::TNodePredicateDataType::New(); + mitk::NodePredicateAnd::Pointer isBinary3dImage = mitk::NodePredicateAnd::New( mitk::NodePredicateAnd::New( isImagePredicate, isBinaryPredicate ), mitk::NodePredicateDimension::New(3)); + + m_Controls->m_MaskBox->SetDataStorage(this->GetDataStorage()); + m_Controls->m_MaskBox->SetPredicate( isBinary3dImage ); + m_Controls->m_MaskBox->SetZeroEntryText("--"); + UpdateGUI(); } } void QmitkDipyReconstructionsView::OnSelectionChanged(berry::IWorkbenchPart::Pointer, const QList& ) { } void QmitkDipyReconstructionsView::UpdateGUI() { if (m_Controls->m_ImageBox->GetSelectedNode().IsNotNull()) m_Controls->m_StartButton->setEnabled(true); else m_Controls->m_StartButton->setEnabled(false); m_Controls->m_ShoreBox->setVisible(false); m_Controls->m_SfmBox->setVisible(false); m_Controls->m_CsdBox->setVisible(false); m_Controls->m_CsaBox->setVisible(false); m_Controls->m_OpdtBox->setVisible(false); switch(m_Controls->m_ModelBox->currentIndex()) { case 0: { m_Controls->m_ShoreBox->setVisible(true); break; } case 1: { m_Controls->m_SfmBox->setVisible(true); break; } case 2: { m_Controls->m_CsdBox->setVisible(true); break; } case 3: { m_Controls->m_CsaBox->setVisible(true); break; } case 4: { m_Controls->m_OpdtBox->setVisible(true); break; } } } void QmitkDipyReconstructionsView::SetFocus() { UpdateGUI(); m_Controls->m_StartButton->setFocus(); } void QmitkDipyReconstructionsView::StartFit() { mitk::DataNode::Pointer node = m_Controls->m_ImageBox->GetSelectedNode(); mitk::Image::Pointer input_image = dynamic_cast(node->GetData()); // get python script as string QString data; QString fileName(":/QmitkDiffusionImaging/dipy_reconstructions.py"); QFile file(fileName); if(!file.open(QIODevice::ReadOnly)) { qDebug()<<"filenot opened"<Size(); ++i) { bvals += boost::lexical_cast(bvaluevector.at(i)); if (bvaluevector.at(i)==0) b0_count++; auto g = gcont->GetElement(i); if (g.two_norm()>0.000001) g /= g.two_norm(); bvecs += "[" + boost::lexical_cast(g[0]) + "," + boost::lexical_cast(g[1]) + "," + boost::lexical_cast(g[2]) + "]"; if (iSize()-1) { bvals += ", "; bvecs += ", "; } } bvals += "]"; bvecs += "]"; if (b0_count==0) { QMessageBox::warning(nullptr, "Error", "No b=0 volume found. Do your b-values need rounding? Use the Preprocessing View for rounding b-values,", QMessageBox::Ok); return; } us::ModuleContext* context = us::GetModuleContext(); us::ServiceReference m_PythonServiceRef = context->GetServiceReference(); mitk::IPythonService* m_PythonService = dynamic_cast ( context->GetService(m_PythonServiceRef) ); mitk::IPythonService::ForceLoadModule(); m_PythonService->Execute("import SimpleITK as sitk"); m_PythonService->Execute("import SimpleITK._SimpleITK as _SimpleITK"); m_PythonService->Execute("import numpy"); m_PythonService->CopyToPythonAsSimpleItkImage( input_image, "in_image"); + m_PythonService->Execute("mask=None"); + if (m_Controls->m_MaskBox->GetSelectedNode().IsNotNull()) + { + auto mitk_mask = dynamic_cast(m_Controls->m_MaskBox->GetSelectedNode()->GetData()); + if (mitk_mask->GetLargestPossibleRegion().GetSize()==input_image->GetLargestPossibleRegion().GetSize()) + m_PythonService->CopyToPythonAsSimpleItkImage( mitk_mask, "mask"); + else + MITK_INFO << "Mask image not used. Does not match data size: " << mitk_mask->GetLargestPossibleRegion().GetSize() << " vs. " + << input_image->GetLargestPossibleRegion().GetSize(); + } + + m_PythonService->Execute("normalize_peaks=False"); + if (m_Controls->m_NormalizePeaks->isChecked()) + m_PythonService->Execute("normalize_peaks=True"); + std::string model = "3D-SHORE"; switch(m_Controls->m_ModelBox->currentIndex()) { case 0: { model = "3D-SHORE"; m_PythonService->Execute("radial_order=" + boost::lexical_cast(m_Controls->m_RadialOrder->value())); m_PythonService->Execute("zeta=" + boost::lexical_cast(m_Controls->m_Zeta->value())); m_PythonService->Execute("lambdaN=" + m_Controls->m_LambdaN->text().toStdString()); m_PythonService->Execute("lambdaL=" + m_Controls->m_LambdaL->text().toStdString()); break; } case 1: { model = "SFM"; m_PythonService->Execute("fa_thr=" + boost::lexical_cast(m_Controls->m_FaThresholdSfm->value())); break; } case 2: { model = "CSD"; m_PythonService->Execute("sh_order=" + boost::lexical_cast(m_Controls->m_ShOrderCsd->value())); m_PythonService->Execute("fa_thr=" + boost::lexical_cast(m_Controls->m_FaThresholdCsd->value())); break; } case 3: { model = "CSA-QBALL"; m_PythonService->Execute("sh_order=" + boost::lexical_cast(m_Controls->m_ShOrderCsa->value())); m_PythonService->Execute("smooth=" + boost::lexical_cast(m_Controls->m_LambdaCsa->value())); break; } case 4: { model = "Opdt"; m_PythonService->Execute("sh_order=" + boost::lexical_cast(m_Controls->m_ShOrderOpdt->value())); m_PythonService->Execute("smooth=" + boost::lexical_cast(m_Controls->m_LambdaOpdt->value())); break; } } m_PythonService->Execute("model_type='"+model+"'"); - m_PythonService->Execute("calculate_peak_image=False"); + + m_PythonService->Execute("num_peaks=0"); + if (m_Controls->m_DoCalculatePeaks->isChecked()) + { + m_PythonService->Execute("num_peaks=" + boost::lexical_cast(m_Controls->m_NumPeaks->value())); + m_PythonService->Execute("min_separation_angle=" + boost::lexical_cast(m_Controls->m_SepAngle->value())); + m_PythonService->Execute("relative_peak_threshold=" + boost::lexical_cast(m_Controls->m_RelativeThreshold->value())); + } + m_PythonService->Execute("data=False"); m_PythonService->Execute("bvals=" + bvals); m_PythonService->Execute("bvecs=" + bvecs); m_PythonService->Execute(data.toStdString(), mitk::IPythonService::MULTI_LINE_COMMAND); // clean up after running script (better way than deleting individual variables?) if(m_PythonService->DoesVariableExist("in_image")) m_PythonService->Execute("del in_image"); // check for errors if(!m_PythonService->GetVariable("error_string").empty()) { QMessageBox::warning(nullptr, "Error", QString(m_PythonService->GetVariable("error_string").c_str()), QMessageBox::Ok); return; } if (m_PythonService->DoesVariableExist("odf_image")) { mitk::OdfImage::ItkOdfImageType::Pointer itkImg = mitk::OdfImage::ItkOdfImageType::New(); mitk::Image::Pointer out_image = m_PythonService->CopySimpleItkImageFromPython("odf_image"); mitk::CastToItkImage(out_image, itkImg); mitk::OdfImage::Pointer image = mitk::OdfImage::New(); image->InitializeByItk( itkImg.GetPointer() ); image->SetVolume( itkImg->GetBufferPointer() ); mitk::DataNode::Pointer odfs = mitk::DataNode::New(); odfs->SetData( image ); QString name(node->GetName().c_str()); odfs->SetName(name.toStdString() + "_" + model); GetDataStorage()->Add(odfs, node); m_PythonService->Execute("del odf_image"); } + + if (m_Controls->m_DoCalculatePeaks->isChecked() && m_PythonService->DoesVariableExist("peak_image")) + { + mitk::Image::Pointer out_image = m_PythonService->CopySimpleItkImageFromPython("peak_image"); + itk::VectorImage::Pointer vectorImage = itk::VectorImage::New(); + mitk::CastToItkImage(out_image, vectorImage); + + itk::VectorImageToFourDImageFilter< float >::Pointer converter = itk::VectorImageToFourDImageFilter< float >::New(); + converter->SetInputImage(vectorImage); + converter->GenerateData(); + mitk::PeakImage::ItkPeakImageType::Pointer itk_peaks = converter->GetOutputImage(); + + mitk::Image::Pointer mitk_peaks = dynamic_cast(mitk::PeakImage::New().GetPointer()); + mitk::CastToMitkImage(itk_peaks, mitk_peaks); + mitk_peaks->SetVolume(itk_peaks->GetBufferPointer()); + + mitk::DataNode::Pointer seg = mitk::DataNode::New(); + seg->SetData( mitk_peaks ); + seg->SetName("Peaks"); + GetDataStorage()->Add(seg, node); + m_PythonService->Execute("del peak_image"); + } } diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsView.h b/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsView.h index f305993b4e..f9186b3db9 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsView.h +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsView.h @@ -1,72 +1,72 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include "ui_QmitkDipyReconstructionsViewControls.h" #include #include - +#include /*! \brief */ class QmitkDipyReconstructionsView : public QmitkAbstractView { // this is needed for all Qt objects that should have a Qt meta-object // (everything that derives from QObject and wants to have signal/slots) Q_OBJECT public: static const std::string VIEW_ID; typedef itk::VectorImage< short, 3 > ItkDwiType; typedef mitk::GradientDirectionsProperty GradProp; QmitkDipyReconstructionsView(); virtual ~QmitkDipyReconstructionsView(); virtual void CreateQtPartControl(QWidget *parent) override; void SetFocus() override; protected slots: void StartFit(); void UpdateGUI(); ///< update button activity etc. dpending on current datamanager selection protected: /// \brief called by QmitkAbstractView when DataManager's selection has changed virtual void OnSelectionChanged(berry::IWorkbenchPart::Pointer part, const QList& nodes) override; Ui::QmitkDipyReconstructionsViewControls* m_Controls; private: void UpdateRegistrationStatus(); ///< update textual status display of the Registration process // the Qt parent of our GUI (NOT of this object) QWidget* m_Parent; }; diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsViewControls.ui b/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsViewControls.ui index 70b1251024..6285b76025 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsViewControls.ui +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.python/src/internal/QmitkDipyReconstructionsViewControls.ui @@ -1,551 +1,669 @@ QmitkDipyReconstructionsViewControls 0 0 435 - 803 + 1036 Form QCommandLinkButton:disabled { border: none; } QGroupBox { background-color: transparent; } 25 - - - - Qt::Vertical - - - - 20 - 40 - + + + + QFrame::NoFrame - - - - - - Constrained Spherical Deconvolution Parameters + + QFrame::Raised - + - 6 + 0 - 6 + 0 - 6 + 0 - 6 + 0 - - - - SH Order: - - - - - - - QFrame::NoFrame - - - QFrame::Raised - - - - 0 + + + + + 3D-SHORE - - 0 + + + + Sparse Fascicle Model - - 0 + + + + Constrained Spherical Deconvolution - - 0 + + + + CSA-QBALL - - 0 + + + + Orientation Probability Density Transform - + - - - - 2 - - - 100 - - - 2 - - - 6 + + + + Input Image: - + - FA Threshold: + Model: - - - - 3 - - - 1.000000000000000 - - - 0.100000000000000 - - - 0.700000000000000 - - + + - - - - - - - Sparse Fascicle Model Parameters - - - - 6 - - - 6 - - - 6 - - - 6 - - - + + - FA Threshold: + Mask Image: - - - - 3 - - - 1.000000000000000 - - - 0.100000000000000 - - - 0.700000000000000 - - + + 3D-SHORE Parameters 6 6 6 6 2 100 2 6 Zeta: Radial Order: LambdaN: LambdaL: 9999 700 1e-8 1e-8 - - - - QFrame::NoFrame - - - QFrame::Raised + + + + Orientation Probability Density Transform Parameters - + - 0 + 6 - 0 + 6 - 0 + 6 - 0 + 6 + + + + QFrame::NoFrame + + + QFrame::Raised + + + + 0 + + + 0 + + + 0 + + + 0 + + + 0 + + + + - + + + 2 + + + 100 + + + 2 + + + 6 + + - - + + - Input Image: + Lambda: - - + + - Model: + SH Order: - - - - 3D-SHORE - - - - - Sparse Fascicle Model - - - - - Constrained Spherical Deconvolution - - - - - CSA-QBALL - - - - - Orientation Probability Density Transform - - + + + 4 + + + 1.000000000000000 + + + 0.001000000000000 + + + 0.006000000000000 + + + + + + + + + + false + + + + + + Start Reconstruction + + + + + + + Qt::Vertical + + + + 20 + 40 + + + + + + + + Sparse Fascicle Model Parameters + + + + 6 + + + 6 + + + 6 + + + 6 + + + + + FA Threshold: + + + + + + + 3 + + + 1.000000000000000 + + + 0.100000000000000 + + + 0.700000000000000 + CSA-QBALL Parameters 6 6 6 6 QFrame::NoFrame QFrame::Raised 0 0 0 0 0 2 100 2 6 Lambda: SH Order: 4 1.000000000000000 0.001000000000000 0.006000000000000 - - - - false - - - - - - Start Reconstruction - - - - - + + - Orientation Probability Density Transform Parameters + Constrained Spherical Deconvolution Parameters - + 6 6 6 6 - - + + + + SH Order: + + + + + QFrame::NoFrame QFrame::Raised - + 0 0 0 0 0 - + 2 100 2 6 - - + + - Lambda: + FA Threshold: - - + + + + 3 + + + 1.000000000000000 + + + 0.100000000000000 + + + 0.700000000000000 + + + + + + + + + + Extract Peaks + + + + + + 1 + + + 100 + + + 1 + + + 3 + + + + + - SH Order: + Relative Threshold: - - - - 4 + + + + Min. Separation Angle: + + + + + + + + + + + + + + 0 + + + 90 + + + 1 + + + 15 + + + + 1.000000000000000 - 0.001000000000000 + 0.100000000000000 - 0.006000000000000 + 0.400000000000000 + + + + + + + Calculate Peaks: + + + + + + + Max. Peaks: + + + + + + + Mas. Normalize Peaks: + + + + + + + QmitkDataStorageComboBox QComboBox
QmitkDataStorageComboBox.h
 + + QmitkDataStorageComboBoxWithSelectNone + QComboBox +
QmitkDataStorageComboBoxWithSelectNone.h
+