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 2b6bd56cf8..073fb07f5a 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox @@ -1,132 +1,136 @@ /** \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. \image html prepro1.png Preprocessing +\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. + \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 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. \image html residuals.png The residual widget 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 \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 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. \image html fslsave.png Save a dwi dataset as fsl \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. 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 */