diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox index 252dc1d22c..cfe82efb54 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox @@ -1,124 +1,124 @@ /** \bundlemainpage{org_diffusion} MITK Diffusion Imaging (MITK-DI) 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. Available sections: - \ref QmitkDiffusionImagingUserManualIssues - \ref QmitkDiffusionImagingUserManualPreprocessing - \ref QmitkDiffusionImagingUserManualTensorReconstruction - \ref QmitkDiffusionImagingUserManualQBallReconstruction - \ref QmitkDiffusionImagingUserManualDicomImport - \ref QmitkDiffusionImagingUserManualQuantification - \ref QmitkDiffusionImagingUserManualVisualizationSettings - \ref QmitkDiffusionImagingUserManualReferences - \ref QmitkDiffusionImagingUserManualTechnicalDetail - \ref QmitkDiffusionImagingUserManualSubManuals \image html overview.png The MITK Diffusion Imaging Module \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 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. \image html prepro1.png Preprocessing \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. \image html tensor1.png ITK tensor reconstruction A few seconds (depending on the image size) after the reconstruction button is hit, a colored image should appear in the main window. \image html tensor4.png Tensor image after reconstruction 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 bundle 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 \image html qballs1.png The q-ball resonstruction view 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. \image html qballs2.png Advanced q-ball reconstruction settings 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. \image html qballs3.png q-ball image after reconstruction \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. \image html dicom1.png Dicom import 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 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). \image html quantification.png Anisotropy quantification \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: \image html visualization3.png Q-ball image with ODF glyph visibility toggled ON \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. \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 \subpage DiffusionImagingPropertiesPage . \section QmitkDiffusionImagingUserManualSubManuals Manuals of componentes The MITK Diffusion tools consist of further components, which have their own documentation, see: \li \subpage org_fiberprocessing \li \subpage org_gibbstracking \li \subpage org_odfdetails \li \subpage org_pvanalysis \li \subpage screenshot_maker \li \subpage org_stochastictracking \li \subpage org_ivim \li \subpage org_brainnetworkanalysis - + \li \subpage org_tractbasedspatialstatistics */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkTbssViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkTbssViewUserManual.dox index e213b09d22..26b49bbb0a 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkTbssViewUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkTbssViewUserManual.dox @@ -1,61 +1,59 @@ /** -\bundlemainpage{org.mitk.views.tractbasedspatialstatistics} The TBSS Module +\page org_tractbasedspatialstatistics The TBSS Module \image html tbss.png "Icon of the Module" \section QmitkTractbasedSpatialStatistics Summary This module can be used to locally explore data resulting from preprocessing with the TBSS module of FSL This document will tell you how to use this module, but it is assumed that you already know how to use MITK in general and how to work with the TBSS module of FSL. If you encounter problems using the module, please have a look at the \ref QmitkTractbasedSpatialStatisticsUserManualTrouble page. Sections: - \ref QmitkTractbasedSpatialStatisticsUserManualOverview - \ref QmitkTractbasedSpatialStatisticsUserManualFSLImport - \ref QmitkTractbasedSpatialStatisticsUserManualRois - \ref QmitkTractbasedSpatialStatisticsUserManualProfiles - \ref QmitkTractbasedSpatialStatisticsUserManualTroubleshooting - \ref QmitkTractbasedSpatialStatisticsUserManualReferences \section QmitkTractbasedSpatialStatisticsUserManualOverview Overview This module is currently under development and as such the interface as well as the capabilities are likely to change significantly between different versions. This documentation describes the features of this current version. \section QmitkTractbasedSpatialStatisticsUserManualFSLImport FSL Import The FSL import allows to import data that has been preprocessed by FSL. FSL creates output images that typically have names like all_FA_skeletonized.nii.gz that are 4-dimensional images that contain registered images of all subjects. By loading this 4D image into the datamanager and listing the groups with the correct number of subjects, in the order of occurrence in the 4D image, in the TBSS-View using the Add button and clicking the import subject data a TBSS file is created that contains all the information needed for tract analysis. The diffusion measure of the image can be set as well. \image html fslimport.png "FSL Import" \section QmitkTractbasedSpatialStatisticsUserManualRois Regions of Interest (ROIs) To create a ROI the mean FA skeleton (typically called mean_FA_skeleton.nii.gz) that is created by FSL should be loaded in to the datamanager and selected. By using the Pointlistwidget points should be set on the skeleton (make sure to select points with relatively high FA values). Points are set by first selecting the button with the '+' and than shift-leftclick in the image. When the correct image is selected in the datamanager the Create ROI button is enabled. Clicking this will create a region of interest that passes through the previously selected points. The roi appears in the datamanager. Before doing so, the name of the roi and the information on the structure on which the ROI lies can be set. This will be saved as extra information in the roi-image. Before the ROI is calculated, a pop-up window will ask the user to provide a threshold value. This should be the same threshold that was previously used in FSL to create a binary mask of the FA skeleton. When this is not done correctly, the region of interest will possible contain zero-valued voxels. \image html tbssRoi.png "Regions of Interest (ROIs)" \section QmitkTractbasedSpatialStatisticsUserManualProfiles y selecting a tbss image with group information and a region of interest image (as was created in a previous stap). A profile plot is drawn in the plot canvas. By clicking in the graph the crosshairs jump to the corresponding location in the image. \image html profiles.png "Profile plots" \section QmitkTractbasedSpatialStatisticsUserManualTroubleshooting 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. \section QmitkTractbasedSpatialStatisticsUserManualReferences References 1. S.M. Smith, M. Jenkinson, H. Johansen-Berg, D. Rueckert, T.E. Nichols, C.E. Mackay, K.E. Watkins, O. Ciccarelli, M.Z. Cader, P.M. Matthews, and T.E.J. Behrens. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage, 31:1487-1505, 2006. -2. S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-219, 2004. \ No newline at end of file +2. S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-219, 2004. +*/ \ No newline at end of file