diff --git a/Documentation/Doxygen/UserManual/MITKPluginManualsList.dox b/Documentation/Doxygen/UserManual/MITKPluginManualsList.dox index 884e29b1c9..be03f2ba30 100644 --- a/Documentation/Doxygen/UserManual/MITKPluginManualsList.dox +++ b/Documentation/Doxygen/UserManual/MITKPluginManualsList.dox @@ -1,45 +1,46 @@ /** \page PluginListPage MITK Plugin Manuals \section PluginListPageOverview Overview The plugins and bundles provide much of the extended functionality of MITK. Each encapsulates a solution to a problem and associated features. This way one can easily assemble the necessary capabilites for a workflow without adding a lot of bloat, by combining plugins as needed. The distinction between developer and end user use is for convenience only and mainly distinguishes which group a plugin is primarily aimed at. \section PluginListPageEndUserPluginList List of Plugins for End User Use \li \subpage org_blueberry_ui_qt_log \li \subpage org_mitk_views_basicimageprocessing \li \subpage org_mitk_views_cmdlinemodules \li \subpage org_mitk_views_datamanager \li \subpage org_mitk_gui_qt_dicom \li \subpage org_mitk_gui_qt_diffusionimaging \li \subpage org_mitk_views_imagecropper \li \subpage org_mitk_views_imagenavigator \li \subpage org_mitk_gui_qt_measurementtoolbox \li \subpage org_mitk_views_meshdecimation \li \subpage org_mitk_views_moviemaker + \li \subpage org_mitk_views_screenshotmaker \li \subpage org_mitk_views_pointsetinteraction \li \subpage org_mitk_gui_qt_python \li \subpage org_mitk_gui_qt_registration \li \subpage org_mitk_gui_qt_remeshing \li \subpage org_mitk_views_segmentation \li \subpage org_mitk_gui_qt_ultrasound \li \subpage org_mitk_gui_qt_viewnavigator \li \subpage org_mitk_views_volumevisualization \li \subpage org_mitk_gui_qt_xnat \section PluginListPageDevPluginList List of Plugins for Developer Use and Examples \li \subpage org_surfacematerialeditor \li \subpage org_toftutorial \li \subpage org_mitk_gui_qt_examples \li \subpage org_mitkexamplesopencv \li \subpage org_mitk_gui_qt_igtexample \li \subpage org_mitk_gui_qt_igttracking \li \subpage org_blueberry_ui_qt_objectinspector \li \subpage org_mitk_gui_qt_eventrecorder */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Connectomics/ConnectomicsManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Connectomics/ConnectomicsManual.dox index 78c187f6b3..ef911183fc 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Connectomics/ConnectomicsManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Connectomics/ConnectomicsManual.dox @@ -1,108 +1,153 @@ /** \page org_mitk_diffusionimagingapp_perspectives_connectomics The Connectomics Perspective \imageMacro{connectomics_perspective.png,"Icon of the Connectomics Perspective",2.00} The connectomics perspective is a collection of views which provide functionality for the work with brain connectivity networks. Currently there exist the following views: \subpage org_mitk_views_connectomicsdata provides network generation either from data or synthetically. \subpage org_mitk_views_connectomicsnetworkoperations provides functionalies to operate and process on networks and other data. \subpage org_mitk_views_connectomicsstatistics provides statistical measures for networks. +\subpage org_mitk_views_randomparcellationview provides a method to randomly parcellate a segmentation using different criteria + \section org_mitk_diffusionimagingapp_perspectives_connectomicsNetworkRenderingCustomization Network Rendering Customization The rendering of the connectomics networks can be customized by changing the associated properties using the property list. A selection of possible options are: \section org_mitk_diffusionimagingapp_perspectives_connectomicsTrouble Troubleshooting No known problems. All other problems.
Please report to the MITK mailing list. See http://www.mitk.org/wiki/Mailinglist on how to do this. */ /** \page org_mitk_views_connectomicsdata The Connectomics Network Data View \imageMacro{QmitkConnectomicsDataViewIcon_48.png,"Icon of the Connectomics Network Data View",2.00} This view can be used to create a network from a parcellation and a fiber image as well as to create synthetic networks. \imageMacro{dataview.png,"The user interface",4.85} To create a network select first a parcellation of the brain (e.g. as provided by freesurfer ) by CTRL+Leftclick and secondly a fiber image ( as created using a tractography view). Then click on the "Create Network" button. Additionally you have the option to create artificial networks, for testing purposes. Currently choices are: */ /** \page org_mitk_views_connectomicsnetworkoperations The Connectomics Network Operations View \imageMacro{QmitkConnectomicsNetworkOperationsViewIcon_48.png,"Icon of the Connectomics Network Operations View",2.00} This view can be used modify networks and related data. \imageMacro{operationsview.png,"The user interface",4.61} Select a parcellation and press "Convert to RGBA" to create a RGBA image. By doing this conversion it is much easier to discern the different parcels. Furthermore MITK supports 3D visualization of an RGBA image. Select a network and press "Create Connectivity Matrix Image" to create a 2D image of the connectivity matrix. By default the weight of a connection is used as grey value. Using the "Rescale" option will rescale the weights so highest one is 255. Using the "Binary" option will result in a binary connectivity matrix. */ /** \page org_mitk_views_connectomicsstatistics The Connectomics Statistics View \imageMacro{QmitkConnectomicsStatisticsViewIcon_48.png,"Icon of the Connectomics Statistics View",2.00} This view can be used to show statistical analysis of a network. \imageMacro{statisticsview.png,"The user interface",6.58} To calculate network statistics select a network in the datamanager. At this time the following statistics are calculated for the entire network: Furthermore some statistics are calculated on a per node basis and displayed as histograms: */ + +/** +\page org_mitk_views_randomparcellationview The Random Parcellation View + +\imageMacro{QmitkRandomParcellationIcon.png,"Icon of the Random Parcellation View",2.00} + +\tableofcontents + +\section org_mitk_views_randomparcellationviewSummary Summary +This view is used to create a random parcellation of a segmented image. +It was designed to parcellate grey matter to acquire random nodes for network analysis. +This document describes how to use this view. + +\section org_mitk_views_randomparcellationviewOverview Overview + +When parcellating an image you can either do a parcellation and use the generated parcellation. In which case it will have as many parcellation as seed points were specified. Instead you can choose to merge some of the original parcels untill either none below a threshold size remain, or until a specified minimum number of parcels has been reached. +A further option is to "Just Merge Small Parcels". This will result in only neighbouring parcels below a threshold size being merged. Using this option may result in final parcels below the given threshold size if they are not adjacent to another parcel below the threshold size. + +In all there are four possibilities to get a random parcellation + + +\section org_mitk_views_randomparcellationviewUsage Usage + +First you have to get a segmentation of the target structure. You can use the Segmentation View to create one. +Select it in the Data Manager. Now you can choose which of the possible ways of parcellation you would like. +See \ref org_mitk_views_randomparcellationviewOverview for these possibilities. +Start the parcellation process using the "Select Random Nodes" button. +You will get a new image in the Data Manager. Every voxel of a given parcel will have the same value (0 for background). + +\section org_mitk_views_randomparcellationviewMethods Methods + +This Plug-In uses a kind of region growing algorithm. Random seed voxels are set on the segmentation and each is an own region at the beginning. Then voxels are added iteratively to each smallest region, first just the 6-connected ones and later the 26-connected ones and isolated voxels. +Specific constraints have to be fulfilled before a voxel is added to a region. +The algorithm stops when all voxels are contained in the regions. +For merging it is checked which regions are neighbors of the smallest parcel and then a suitable one, +according to a cost function, is merged to this parcel. When the merging algorithm stops depends on which stop-mechanism is chosen. + +*/ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Connectomics/QmitkRandomParcellationIcon.png b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Connectomics/QmitkRandomParcellationIcon.png new file mode 100644 index 0000000000..10728e8e8a Binary files /dev/null and b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Connectomics/QmitkRandomParcellationIcon.png differ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/DataImport/QmitkDiffusionImagingDataImportPage.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/DataImport/QmitkDiffusionImagingDataImportPage.dox new file mode 100644 index 0000000000..26cb3a4fa3 --- /dev/null +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/DataImport/QmitkDiffusionImagingDataImportPage.dox @@ -0,0 +1,24 @@ +/** +\page QmitkDiffusionImagingDataImportPage Importing Data + +\section QmitkDiffusionImagingUserManualDicomImport Dicom Import + +The dicom import does not cover all hardware manufacturers but only Siemens dicom images. MITK-DI is also capable of reading the nrrd format, which is documented elsewhere [1, 2]. These files can be created by combining the raw image data with a corresponding textual header file. The file extension should be changed from *.nrrd to *.dwi or from *.nhdr to *.hdwi respectively in order to let MITK-DI recognize the diffusion related header information provided in the files. + +In case your dicom images are readable by MITK-DI, select one or more input dicom folders and click import. Each input folder must only contain DICOM-images that can be combined into one vector-valued 3D output volume. Different patients must be loaded from different input-folders. The folders must not contain other acquisitions (e.g. T1,T2,localizer). + +In case many imports are performed at once, it is recommended to set the the optional output folder argument. This prevents the images from being kept in memory. + +\imageMacro{dicom1.png,"Dicom import",9.59} + +The option "Average duplicate gradients" accumulates the information that was acquired with multiple repetitions for one gradient. Vectors do not have to be precisely equal in order to be merged, if a "blur radius" > 0 is configured. + +\section QmitkDiffusionImagingUserManualFslImport FSL Import + +FSL diffusion data can be imported with MITK Diffusion. FSL diffusion datasets consist of 3 files: a nifty file (filename.nii.gz or filename.nii), a bvecs file (filename.bvecs), which is a text file containing the gradient vectors, and a bvals file (filename.bvecs), containing the b-values. Due to the system that selects suitable file readers, MITK will not recognize these files as diffusion datasets. In order to make MITK recognize it as diffusion, the extension must be changed from .nii.gz to .fslgz (so the new name is filename.fslgz) or from filename.nii to filename.fsl. The bvecs and bvals files have to be renamed as well(to filename.fsl.bvecs/filenames.fsl.bvecs or to filename.fslgz.bvecs/filename.fslgz.bvals). + +MITK can also save diffusion weighted images in FSL format. To do this the extension of the new file should be changed to .fsl or .fslgz upon saving the file. + +\imageMacro{fslsave.png,"Save a dwi dataset as fsl",16.00} + +*/ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/dicom1.png b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/DataImport/dicom1.png similarity index 100% rename from Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/dicom1.png rename to Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/DataImport/dicom1.png diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/fslsave.png b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/DataImport/fslsave.png similarity index 100% rename from Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/fslsave.png rename to Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/DataImport/fslsave.png diff --git 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b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkDiffusionImagingPreprocessingPage.dox new file mode 100644 index 0000000000..ea1537cd97 --- /dev/null +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkDiffusionImagingPreprocessingPage.dox @@ -0,0 +1,9 @@ +/** +\page QmitkDiffusionImagingPreprocessingPage Preprocessing of Diffusion Images + +\section QmitkDiffusionImagingPreprocessingPagePreprocessing Preprocessing + +The preprocessing view gives an overview over the important features of a diffusion weighted image like the number of gradient directions, b-value and the measurement frame. Additionally it allows the extraction of the B0 image, reduction of gradient directions and the generation of a binary brain mask. The image volume can be modified by applying a new mesurement frame, which is useful if the measurement frame is not set correctly in the image header, or by averaging redundant gradient directions. + +\imageMacro{prepro1.png,"Preprocessing",9.97} +*/ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkDiffusionImagingReconstruction.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkDiffusionImagingReconstruction.dox new file mode 100644 index 0000000000..11005d5672 --- /dev/null +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkDiffusionImagingReconstruction.dox @@ -0,0 +1,39 @@ +/** +\page QmitkDiffusionImagingReconstructionPage Reconstruction + +\section QmitkDiffusionImagingUserManualTensorReconstruction Tensor Reconstruction + +The tensor reconstruction view allows ITK based tensor reconstruction [3]. The advanced settings for ITK reconstruction let you configure a manual threshold on the non-diffusion weighted image. All voxels below this threshold will not be reconstructed and left blank. It is also possible to check for negative eigenvalues. The according voxels are also left blank. + +\imageMacro{tensor1.png,"ITK tensor reconstruction", 9.97} + +A few seconds (depending on the image size) after the reconstruction button is hit, a colored image should appear in the main window. + +\imageMacro{tensor4.png,"Tensor image after reconstruction",16.00} + +To assess the quality of the tensor fit it has been proposed to calculate the model residual [9]. This calculates the residual between the measured signal and the signal predicted by the model. Large residuals indicate an inadequacy of the model or the presence of artefacts in the signal intensity (noise, head motion, etc.). To use this option: Select a DWI dataset, estimate a tensor, select both the DWI node and the tensor node in the datamanager and press Residual Image Calculation. MITK-Diffusion can show the residual for every voxel averaged over all volumes or (in the plot widget) summarized per volume or for every slice in every volume. Clicking in the widget where the residual is shown per slice will automatically let the cross-hair jump to that position in the DWI dataset. If Percentage of outliers is checked, the per volume plot will show the percentage of outliers per volume. Otherwise it will show the mean together with the first and third quantile of residuals. See [9] for more information. + +\imageMacro{residuals.png,"The residual widget",16.00} + +The view also allows the generation of artificial diffusion weighted or Q-Ball images from the selected tensor image. The ODFs of the Q-Ball image are directly initialized from the tensor values and afterwards normalized. The diffusion weighted image is estimated using the l2-norm image of the tensor image as B0. The gradient images are afterwards generated using the standard tensor equation. + +\section QmitkDiffusionImagingUserManualQBallReconstruction Q-Ball Reconstruction + +The q-ball reonstruction view implements a variety of reconstruction methods. The different reconstruction methods are described in the following: +\li Numerical: The original, numerical q-ball reconstruction presented by Tuch et al. [5] +\li Standard (SH): Descoteaux's reconstruction based on spherical harmonic basis functions [6] +\li Solid Angle (SH): Aganj's reconstruction with solid angle consideration [7] +\li ADC-profile only: The ADC-profile reconstructed with spherical harmonic basis functions +\li Raw signal only: The raw signal reconstructed with spherical harmonic basis functions + +\imageMacro{qballs1.png,"The q-ball resonstruction view",9.99} + +B0 threshold works the same as in tensor reconstruction. The maximum l-level configures the size of the spherical harmonics basis. Larger l-values (e.g. l=8) allow higher levels of detail, lower levels are more stable against noise (e.g. l=4). Lambda is a regularisation parameter. Set it to 0 for no regularisation. lambda = 0.006 has proven to be a stable choice under various settings. + +\imageMacro{qballs2.png,"Advanced q-ball reconstruction settings",8.00} + +This is how a q-ball image should initially look after reconstruction. Standard q-balls feature a relatively low GFA and thus appear rather dark. Adjust the level-window to solve this. + +\imageMacro{qballs3.png,"q-ball image after reconstruction",16.00} + +*/ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkDiffusionImagingRegistration.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkDiffusionImagingRegistration.dox new file mode 100644 index 0000000000..354700a564 --- /dev/null +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkDiffusionImagingRegistration.dox @@ -0,0 +1,16 @@ +/** +\page QmitkDiffusionImagingDWIRegistrationPage DWI Registration + +\section QmitkDiffusionImagingUserManualDWIRegistration DWI Registration + +The DWI registration view allows head-motion and eddy-current correction. First the b=0 images (if more than 1) are registered and averaged. The averaged image then serves as fixed image for the alignment of the remaining (weighted, b>0) images. + +\imageMacro{registration_basic.png,"Basic DWI Registration Interface",8.00} + +In the basic settings, a selected DW image in the Data Manager is marked as Input Data and the Start Head Motion Correction button is enabled. If more than one DW images are selected, they will be processed in a consecutive manner. For larger sets of dw images to be processed, the Advanced settings can be used. + +\imageMacro{registration_batch.png,"Batch Processing DWI Registration Interface",8.00} + +Here all valid .dwi data located in the given input folder will be processed through the head motion correction and the output will be stored in the current working directory or in a custom folder if specified in the user interface. The output image will have the _MC postfix attached to the input's name. + +*/ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkDiffusionImagingVisualization.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkDiffusionImagingVisualization.dox new file mode 100644 index 0000000000..eff05db5ed --- /dev/null +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkDiffusionImagingVisualization.dox @@ -0,0 +1,19 @@ +/** +\page QmitkDiffusionImagingVisualization ODF Visualization + +\section QmitkDiffusionImagingVisualizationSettings ODF Visualization Setting + +In this small view, the visualization of ODFs and diffusion images can be configured. Depending on the selected image in the data storage, different options are shown here. + +For tensor or q-ball images, the visibility of glyphs in the different render windows (T)ransversal, (S)agittal, and (C)oronal can be configured here. The maximal number of glyphs to display can also be configured here for. This is usefull to keep the system response time during rendering feasible. The other options configure normalization and scaling of the glyphs. + +In diffusion images, a slider lets you choose the desired image channel from the vector of images (each gradient direction one image) for rendering. Furthermore reinit can be performed and texture interpolation toggled. + +This is how a visualization with activated glyphs should look like: + +\imageMacro{visualization3.png,"Q-ball image with ODF glyph visibility toggled ON",16.00} + + + + +*/ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkOdfDetailsViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkOdfDetailsViewUserManual.dox similarity index 100% rename from Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkOdfDetailsViewUserManual.dox rename to Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkOdfDetailsViewUserManual.dox diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkOdfMaximaExtractionViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/QmitkOdfMaximaExtractionViewUserManual.dox similarity index 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b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/visualization3.png similarity index 100% rename from Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/visualization3.png rename to Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Preproc_Recons/visualization3.png diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingPortalPage.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingPortalPage.dox new file mode 100644 index 0000000000..0ba46fa6f0 --- /dev/null +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingPortalPage.dox @@ -0,0 +1,84 @@ +/** +\page org_mitk_gui_qt_diffusionimaging MITK Diffusion Imaging (MITK-DI) + +\tableofcontents + +This module provides means to diffusion weighted image reconstruction, visualization and quantification. Diffusion tensors as well as different q-ball reconstruction schemes are supported. Q-ball imaging aims at recovering more detailed information about the orientations of fibers from diffusion MRI measurements and, in particular, to resolve the orientations of crossing fibers. + +\section org_mitk_gui_qt_diffusionimagingComponents Components + +MITK Diffusion consists of further components, which have their own documentation, see: + +\subsection org_mitk_gui_qt_diffusionimagingComponentsDataImport Data Import + + \li \subpage QmitkDiffusionImagingDataImportPage + +\subsection org_mitk_gui_qt_diffusionimagingComponentsPreprocessingAndReconstruction Preprocessing and Reconstruction + +Register or denoise original data and convert it into tensor or q-ball images. + + \li \subpage QmitkDiffusionImagingDWIRegistrationPage + \li \subpage QmitkDiffusionImagingPreprocessingPage + \li \subpage QmitkDiffusionImagingReconstructionPage + \li \subpage QmitkDiffusionImagingVisualization + \li \subpage org_mitk_views_odfdetails + \li \subpage org_mitk_views_odfmaximaextraction + \li \subpage org_mitk_views_denoisingview + +\subsection org_mitk_gui_qt_diffusionimagingComponentsFiberTracking Fiber Tracking + +Create and work with fiber tractographies. + + \li \subpage org_mitk_views_fiberprocessing + \li \subpage org_mitk_views_gibbstracking + \li \subpage org_mitk_views_stochasticfibertracking + \li \subpage org_mitk_views_fiberextraction + \li \subpage org_mitk_views_fiberprocessing + \li \subpage org_mitk_views_streamlinetracking + \li \subpage org_mitk_views_fiberfoxview + \li \subpage org_mitk_views_fieldmapgenerator + +\subsection org_mitk_gui_qt_diffusionimagingComponentsQuantification Quantification + +Create parameter maps and quantify different properties. + + \li \subpage QmitkDiffusionImagingQuantificationPage + \li \subpage org_mitk_views_ivim + \li \subpage org_mitk_views_partialvolumeanalysisview + \li \subpage org_mitk_views_tractbasedspatialstatistics + +\subsection org_mitk_gui_qt_diffusionimagingComponentsConnectomics Connectomics + +Create and analyse connectome networks. + + \li \subpage org_mitk_diffusionimagingapp_perspectives_connectomics + +\section org_mitk_gui_qt_diffusionimagingIssues Known Issues + +\li Dicom Import: The dicom import has so far only been implemented for Siemens dicom images. MITK-DI is capable of reading the nrrd format, which is documented elsewhere [1, 2]. These files can be created by combining the raw image data with a corresponding textual header file. The file extension should be changed from *.nrrd to *.dwi or from *.nhdr to *.hdwi respectively in order to let MITK-DI recognize the diffusion related header information provided in the files. + +\section org_mitk_gui_qt_diffusionimagingTechnicalDetails Technical Information for Developers + +The diffusion imaging module uses additional properties beside the ones in use in other modules, for further information see \ref DiffusionImagingPropertiesPage . + +\section org_mitk_gui_qt_diffusionimagingReferences References + +Further reading regarding diffusion: + +1. http://teem.sourceforge.net/nrrd/format.html + +2. http://www.cmake.org/Wiki/Getting_Started_with_the_NRRD_Format + +3. C.F.Westin, S.E.Maier, H.Mamata, A.Nabavi, F.A.Jolesz, R.Kikinis, "Processing and visualization for Diffusion tensor MRI", Medical image Analysis, 2002, pp 93-108 + +5. Tuch, D.S., 2004. Q-ball imaging. Magn Reson Med 52, 1358-1372. + +6. Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R., 2007. Regularized, fast, and robust analytical Q-ball imaging. Magn Reson Med 58, 497-510. + +7. Aganj, I., Lenglet, C., Sapiro, G., 2009. ODF reconstruction in q-ball imaging with solid angle consideration. Proceedings of the Sixth IEEE International Symposium on Biomedical Imaging Boston, MA. + +8. Goh, A., Lenglet, C., Thompson, P.M., Vidal, R., 2009. Estimating Orientation Distribution Functions with Probability Density Constraints and Spatial Regularity. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv LNCS 5761, 877 ff. + +9. J.-D. Tournier, S. Mori, A. Leemans., 2011. Diffusion Tensor Imaging and Beyond. Magn Reson Med 65, 1532-1556. + +*/ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox deleted file mode 100644 index 769b664537..0000000000 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox +++ /dev/null @@ -1,145 +0,0 @@ -/** -\page org_mitk_gui_qt_diffusionimaging MITK Diffusion Imaging (MITK-DI) - -\tableofcontents - -This module provides means to diffusion weighted image reconstruction, visualization and quantification. Diffusion tensors as well as different q-ball reconstruction schemes are supported. Q-ball imaging aims at recovering more detailed information about the orientations of fibers from diffusion MRI measurements and, in particular, to resolve the orientations of crossing fibers. - -\section QmitkDiffusionImagingUserManualIssues Known Issues - -\li Dicom Import: The dicom import has so far only been implemented for Siemens dicom images. MITK-DI is capable of reading the nrrd format, which is documented elsewhere [1, 2]. These files can be created by combining the raw image data with a corresponding textual header file. The file extension should be changed from *.nrrd to *.dwi or from *.nhdr to *.hdwi respectively in order to let MITK-DI recognize the diffusion related header information provided in the files. - -\section QmitkDiffusionImagingUserManualPreprocessing Preprocessing - -The preprocessing view gives an overview over the important features of a diffusion weighted image like the number of gradient directions, b-value and the measurement frame. Additionally it allows the extraction of the B0 image, reduction of gradient directions and the generation of a binary brain mask. The image volume can be modified by applying a new mesurement frame, which is useful if the measurement frame is not set correctly in the image header, or by averaging redundant gradient directions. - -\imageMacro{prepro1.png,"Preprocessing",9.97} - -\section QmitkDiffusionImagingUserManualDWIRegistration DWI Registration - -The DWI registration view allows head-motion and eddy-current correction. First the b=0 images (if more than 1) are registered and averaged. The averaged image then serves as fixed image for the alignment of the remaining (weighted, b>0) images. - -\imageMacro{registration_basic.png,"Basic DWI Registration Interface",8.00} - -In the basic settings, a selected DW image in the Data Manager is marked as Input Data and the Start Head Motion Correction button is enabled. If more than one DW images are selected, they will be processed in a consecutive manner. For larger sets of dw images to be processed, the Advanced settings can be used. - -\imageMacro{registration_batch.png,"Batch Processing DWI Registration Interface",8.00} - -Here all valid .dwi data located in the given input folder will be processed through the head motion correction and the output will be stored in the current working directory or in a custom folder if specified in the user interface. The output image will have the _MC postfix attached to the input's name. - -\section QmitkDiffusionImagingUserManualTensorReconstruction Tensor Reconstruction - -The tensor reconstruction view allows ITK based tensor reconstruction [3]. The advanced settings for ITK reconstruction let you configure a manual threshold on the non-diffusion weighted image. All voxels below this threshold will not be reconstructed and left blank. It is also possible to check for negative eigenvalues. The according voxels are also left blank. - -\imageMacro{tensor1.png,"ITK tensor reconstruction", 9.97} - -A few seconds (depending on the image size) after the reconstruction button is hit, a colored image should appear in the main window. - -\imageMacro{tensor4.png,"Tensor image after reconstruction",16.00} - -To assess the quality of the tensor fit it has been proposed to calculate the model residual [9]. This calculates the residual between the measured signal and the signal predicted by the model. Large residuals indicate an inadequacy of the model or the presence of artefacts in the signal intensity (noise, head motion, etc.). To use this option: Select a DWI dataset, estimate a tensor, select both the DWI node and the tensor node in the datamanager and press Residual Image Calculation. MITK-Diffusion can show the residual for every voxel averaged over all volumes or (in the plot widget) summarized per volume or for every slice in every volume. Clicking in the widget where the residual is shown per slice will automatically let the cross-hair jump to that position in the DWI dataset. If Percentage of outliers is checked, the per volume plot will show the percentage of outliers per volume. Otherwise it will show the mean together with the first and third quantile of residuals. See [9] for more information. - -\imageMacro{residuals.png,"The residual widget",16.00} - -The view also allows the generation of artificial diffusion weighted or Q-Ball images from the selected tensor image. The ODFs of the Q-Ball image are directly initialized from the tensor values and afterwards normalized. The diffusion weighted image is estimated using the l2-norm image of the tensor image as B0. The gradient images are afterwards generated using the standard tensor equation. - -\section QmitkDiffusionImagingUserManualQBallReconstruction Q-Ball Reconstruction - -The q-ball reonstruction view implements a variety of reconstruction methods. The different reconstruction methods are described in the following: -\li Numerical: The original, numerical q-ball reconstruction presented by Tuch et al. [5] -\li Standard (SH): Descoteaux's reconstruction based on spherical harmonic basis functions [6] -\li Solid Angle (SH): Aganj's reconstruction with solid angle consideration [7] -\li ADC-profile only: The ADC-profile reconstructed with spherical harmonic basis functions -\li Raw signal only: The raw signal reconstructed with spherical harmonic basis functions - -\imageMacro{qballs1.png,"The q-ball resonstruction view",9.99} - -B0 threshold works the same as in tensor reconstruction. The maximum l-level configures the size of the spherical harmonics basis. Larger l-values (e.g. l=8) allow higher levels of detail, lower levels are more stable against noise (e.g. l=4). Lambda is a regularisation parameter. Set it to 0 for no regularisation. lambda = 0.006 has proven to be a stable choice under various settings. - -\imageMacro{qballs2.png,"Advanced q-ball reconstruction settings",8.00} - -This is how a q-ball image should initially look after reconstruction. Standard q-balls feature a relatively low GFA and thus appear rather dark. Adjust the level-window to solve this. - -\imageMacro{qballs3.png,"q-ball image after reconstruction",16.00} - -\section QmitkDiffusionImagingUserManualDicomImport Dicom Import - -The dicom import does not cover all hardware manufacturers but only Siemens dicom images. MITK-DI is also capable of reading the nrrd format, which is documented elsewhere [1, 2]. These files can be created by combining the raw image data with a corresponding textual header file. The file extension should be changed from *.nrrd to *.dwi or from *.nhdr to *.hdwi respectively in order to let MITK-DI recognize the diffusion related header information provided in the files. - -In case your dicom images are readable by MITK-DI, select one or more input dicom folders and click import. Each input folder must only contain DICOM-images that can be combined into one vector-valued 3D output volume. Different patients must be loaded from different input-folders. The folders must not contain other acquisitions (e.g. T1,T2,localizer). - -In case many imports are performed at once, it is recommended to set the the optional output folder argument. This prevents the images from being kept in memory. - -\imageMacro{dicom1.png,"Dicom import",9.59} - -The option "Average duplicate gradients" accumulates the information that was acquired with multiple repetitions for one gradient. Vectors do not have to be precisely equal in order to be merged, if a "blur radius" > 0 is configured. - -\section QmitkDiffusionImagingUserManualFslImport FSL Import - -FSL diffusion data can be imported with MITK Diffusion. FSL diffusion datasets consist of 3 files: a nifty file (filename.nii.gz or filename.nii), a bvecs file (filename.bvecs), which is a text file containing the gradient vectors, and a bvals file (filename.bvecs), containing the b-values. Due to the system that selects suitable file readers, MITK will not recognize these files as diffusion datasets. In order to make MITK recognize it as diffusion, the extension must be changed from .nii.gz to .fslgz (so the new name is filename.fslgz) or from filename.nii to filename.fsl. The bvecs and bvals files have to be renamed as well(to filename.fsl.bvecs/filenames.fsl.bvecs or to filename.fslgz.bvecs/filename.fslgz.bvals). - -MITK can also save diffusion weighted images in FSL format. To do this the extension of the new file should be changed to .fsl or .fslgz upon saving the file. - -\imageMacro{fslsave.png,"Save a dwi dataset as fsl",16.00} - -\section QmitkDiffusionImagingUserManualQuantification Quantification - -The quantification view allows the derivation of different scalar anisotropy measures for the reconstructed tensors (Fractional Anisotropy, Relative Anisotropy, Axial Diffusivity, Radial Diffusivity) or q-balls (Generalized Fractional Anisotropy). - -\imageMacro{quantification.png,"Anisotropy quantification",2} - -\section QmitkDiffusionImagingUserManualVisualizationSettings ODF Visualization Setting - -In this small view, the visualization of ODFs and diffusion images can be configured. Depending on the selected image in the data storage, different options are shown here. - -For tensor or q-ball images, the visibility of glyphs in the different render windows (T)ransversal, (S)agittal, and (C)oronal can be configured here. The maximal number of glyphs to display can also be configured here for. This is usefull to keep the system response time during rendering feasible. The other options configure normalization and scaling of the glyphs. - -In diffusion images, a slider lets you choose the desired image channel from the vector of images (each gradient direction one image) for rendering. Furthermore reinit can be performed and texture interpolation toggled. - -This is how a visualization with activated glyphs should look like: - -\imageMacro{visualization3.png,"Q-ball image with ODF glyph visibility toggled ON",16.00} - -\section QmitkDiffusionImagingUserManualReferences References - -1. http://teem.sourceforge.net/nrrd/format.html - -2. http://www.cmake.org/Wiki/Getting_Started_with_the_NRRD_Format - -3. C.F.Westin, S.E.Maier, H.Mamata, A.Nabavi, F.A.Jolesz, R.Kikinis, "Processing and visualization for Diffusion tensor MRI", Medical image Analysis, 2002, pp 93-108 - -5. Tuch, D.S., 2004. Q-ball imaging. Magn Reson Med 52, 1358-1372. - -6. Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R., 2007. Regularized, fast, and robust analytical Q-ball imaging. Magn Reson Med 58, 497-510. - -7. Aganj, I., Lenglet, C., Sapiro, G., 2009. ODF reconstruction in q-ball imaging with solid angle consideration. Proceedings of the Sixth IEEE International Symposium on Biomedical Imaging Boston, MA. - -8. Goh, A., Lenglet, C., Thompson, P.M., Vidal, R., 2009. Estimating Orientation Distribution Functions with Probability Density Constraints and Spatial Regularity. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv LNCS 5761, 877 ff. - -9. J.-D. Tournier, S. Mori, A. Leemans., 2011. Diffusion Tensor Imaging and Beyond. Magn Reson Med 65, 1532-1556. - -\section QmitkDiffusionImagingUserManualTechnicalDetail Technical Information for Developers - -The diffusion imaging module uses additional properties beside the ones in use in other modules, for further information see \ref DiffusionImagingPropertiesPage . - -\section QmitkDiffusionImagingUserManualSubManuals Manuals of componentes - -The MITK Diffusion tools consist of further components, which have their own documentation, see: - - \li \subpage org_mitk_views_fiberprocessing - \li \subpage org_mitk_views_gibbstracking - \li \subpage org_mitk_views_odfdetails - \li \subpage org_mitk_views_partialvolumeanalysisview - \li \subpage org_mitk_views_screenshotmaker - \li \subpage org_mitk_views_stochasticfibertracking - \li \subpage org_mitk_views_ivim - \li \subpage org_mitk_diffusionimagingapp_perspectives_connectomics - \li \subpage org_mitk_views_tractbasedspatialstatistics - \li \subpage org_mitk_views_fiberextraction - \li \subpage org_mitk_views_fiberprocessing - \li \subpage org_mitk_views_odfmaximaextraction - \li \subpage org_mitk_views_streamlinetracking - \li \subpage org_mitk_views_fiberfoxview - \li \subpage org_mitk_views_fieldmapgenerator - \li \subpage org_mitk_views_denoisingview -*/ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Quantification/QmitkDiffusionImagingQuantificationPage.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Quantification/QmitkDiffusionImagingQuantificationPage.dox new file mode 100644 index 0000000000..c3ca35f105 --- /dev/null +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Quantification/QmitkDiffusionImagingQuantificationPage.dox @@ -0,0 +1,8 @@ +/** + +\page QmitkDiffusionImagingQuantificationPage Quantification + +The quantification view allows the derivation of different scalar anisotropy measures for the reconstructed tensors (Fractional Anisotropy, Relative Anisotropy, Axial Diffusivity, Radial Diffusivity) or q-balls (Generalized Fractional Anisotropy). + +\imageMacro{quantification.png,"Anisotropy quantification",2} +*/ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkPartialVolumeAnalysisViewManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Quantification/QmitkPartialVolumeAnalysisViewManual.dox similarity index 100% rename from Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkPartialVolumeAnalysisViewManual.dox rename to Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Quantification/QmitkPartialVolumeAnalysisViewManual.dox diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkTbssViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Quantification/QmitkTbssViewUserManual.dox similarity index 100% rename from Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkTbssViewUserManual.dox rename to Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Quantification/QmitkTbssViewUserManual.dox diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkUserIVIMViewManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Quantification/QmitkUserIVIMViewManual.dox similarity index 89% rename from Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkUserIVIMViewManual.dox rename to Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Quantification/QmitkUserIVIMViewManual.dox index 80d9e24921..6549fe1d78 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkUserIVIMViewManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/Quantification/QmitkUserIVIMViewManual.dox @@ -1,41 +1,41 @@ /** \page org_mitk_views_ivim Intra-voxel incoherent motion estimation (IVIM) The required input for the "Intra-voxel incoherent motion estimation" (IVIM) is a diffusion weighted image (.dwi or .hdwi) that was acquired with several different b-values. \imageMacro{ivimview.png,"The IVIM View",13.91} Once an input image is selected in the datamanager, the IVIM view allows for interactive exploration of the dataset (click around in the image and watch the estimated parameters in the figure of the view) as well as generation of f-, D-, and D*-maps (activate the checkmarks and press "Generate Output Images"). The "neglect b<" threshold allows you to ignore b-values smaller then a threshold for the initial fit of f and D. D* is then estimated using all measurements. The exact values of the current fit are always given in the legend underneath the figure. -\section QmitkDiffusionImagingUserManualInputData Region of interest analysis +\section QmitkIVIMViewROIAnalysis Region of interest analysis Create region of interest: To create a new segmentatin, open the "quantification" perspective, select the tab "Segmentation", and create a segmentation of the structure of interest. Alternatively, of course, you may also load a binary image from file or generate your segmentation in any other possible way. IVIM in region of interset: Go back to the "IVIM" perspective and select both, the diffusion image and the segmentation (holding the CTRL key). A red message should appear "Averaging N voxels". -\section QmitkDiffusionImagingUserManualInputData Export +\section QmitkIVIMViewExport Export All model parameters and corresponding curves can be exported to clipboard using the buttons underneath the figure. -\section QmitkDiffusionImagingUserManualInputData Advanced Settings +\section QmitkIVIMViewAdvanced Advanced Settings Advanced users, that know what they are doing, can change the method for the model-fit under "Advanced Settings" on the very bottom of the view. 3-param fit, linear fit of f/D, and fix D* are among the options. -\section QmitkDiffusionImagingUserManualInputData Suggested Readings +\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. 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