MITK Diffusion - Official part of the Medical Imaging Interaction Toolkit
Copyright © German Cancer Research Center (DKFZ), Division of Medical Image Computing (MIC). Please make sure that your usage of this code is in compliance with the code license.
The MITK Diffusion application [1,2] offers a selection of image analysis algorithms for the processing of diffusion-weighted MR images. It encompasses the research of the Division of Medical Image Computing at the German Cancer Research Center (DKFZ).
Please have a look at the requirements for running MITK Diffusion with all its features successfully!
The latest builds come as executable setup wizards that install MITK Diffusion on your system or alternatively as simple .tar.gz or .zip archive where you can execute MITK Diffusion and the command line apps "manually". You can find the newest installers here.
If you encounter any bugs, please report them here on github or use the MITK-users mailing list. We are grateful for any feedback!
For Windows users: MITK Diffusion requires the Microsoft Visual C++ 2017 Redistributable to be installed on the system. The MITK Diffusion installer automatically installs this redistributable for you if not already present on the system, but it needs administrative privileges to do so. So to install the redistributable, run the MITK Diffusion installer as administrator.
Support for most established image formats
Image preprocessing
Diffusion gradient/b-value processing
ODF reconstruction and signal modelling
Quantification of diffusion-weighted/tensor/ODF images
Segmentation
Fiber tractography
Fiber processing
Fiberfox dMRI simulations [10]
Other features
pip3 install cmdint
Screenshot of the MITK Diffusion Welcome Screen
Scalar map visualization
Tensor Visualization
ODF visualization
Peak visualization (uniform white coloring)
Interactive tractography in MITK Diffusion. The tractogram updates automatically on parameter change and movement of the spherical seed region.
Tract dissection using manually drawn ROIs.
Automatic streamline weighting (similar to SIFT2 or LiFE)
Illustration of the dMRI phantom simulation process using Fiberfox.
Illustration of simulated dMRI images with various artifacts (a bit excessive for illustration purposes): eddy current distortions (1), motion and spike (2), intensity drift (3), motion, eddy and noise (4), ringing (5), B0 inhomogeneity distortions (6), from left to right.
Automatically generated random fiber configuration for Fiberfox simulations.
Please not that we moved away from a separate MITK Diffusion application and are instead building configurations of the MITK Workbench. In a (near)-future version of MITK, customizations of the Workbench will be possible to change the display and executable name etc. A workaround is to use the MITK branch https://github.com/MITK/MITK/tree/feature/T30337-WorkbenchCustomizations2, which already enables some of those customizations but the final mechanism fot that will be different.
Install Qt on your system (currently 6.6.1).
Clone MITK from github using Git version control.
Clone MITK Diffusion from github.
Configure the MITK Superbuild using CMake.
Start the Superbuild:
More detailed build instructions can be found in the documentation.
Continuous integration: https://cdash.mitk.org/index.php?project=MITK-Diffusion
All publications of the Division of Medical Image Computing can be found [https://www.dkfz.de/en/mic/publications/ here].
[1] Fritzsche, Klaus H., Peter F. Neher, Ignaz Reicht, Thomas van Bruggen, Caspar Goch, Marco Reisert, Marco Nolden, et al. “MITK Diffusion Imaging.” Methods of Information in Medicine 51, no. 5 (2012): 441.
[2] Fritzsche, K., and H.-P. Meinzer. “MITK-DI A New Diffusion Imaging Component for MITK.” In Bildverarbeitung Für Die Medizin, n.d.
[3] Wasserthal, Jakob, Peter Neher, and Klaus H. Maier-Hein. “TractSeg - Fast and Accurate White Matter Tract Segmentation.” NeuroImage 183 (August 4, 2018): 239–53.
[4] Neher, P. F., B. Stieltjes, M. Reisert, I. Reicht, H.P. Meinzer, and K. Maier-Hein. “MITK Global Tractography.” In SPIE Medical Imaging: Image Processing, 2012.
[5] Chamberland, M., K. Whittingstall, D. Fortin, D. Mathieu, und M. Descoteaux. „Real-time multi-peak tractography for instantaneous connectivity display“. Front Neuroinform 8 (2014): 59. doi:10.3389/fninf.2014.00059.
[6] Neher, Peter F., Marc-Alexandre Côté, Jean-Christophe Houde, Maxime Descoteaux, and Klaus H. Maier-Hein. “Fiber Tractography Using Machine Learning.” NeuroImage. Accessed July 17, 2017. doi:10.1016/j.neuroimage.2017.07.028.
[7] Garyfallidis, Eleftherios, Matthew Brett, Marta Morgado Correia, Guy B. Williams, and Ian Nimmo-Smith. “QuickBundles, a Method for Tractography Simplification.” Frontiers in Neuroscience 6 (2012).
[8] Smith, Robert E., Jacques-Donald Tournier, Fernando Calamante, and Alan Connelly. “SIFT2: Enabling Dense Quantitative Assessment of Brain White Matter Connectivity Using Streamlines Tractography.” NeuroImage 119, no. Supplement C (October 1, 2015): 338–51.
[9] Pestilli, Franco, Jason D. Yeatman, Ariel Rokem, Kendrick N. Kay, and Brian A. Wandell. “Evaluation and Statistical Inference for Human Connectomes.” Nature Methods 11, no. 10 (October 2014): 1058–63.
[10] Neher, Peter F., Frederik B. Laun, Bram Stieltjes, and Klaus H. Maier-Hein. “Fiberfox: Facilitating the Creation of Realistic White Matter Software Phantoms.” Magnetic Resonance in Medicine 72, no. 5 (November 2014): 1460–70. doi:10.1002/mrm.25045.
If you have questions about the application or if you would like to give us feedback, don't hesitate to contact us using our mailing list or, for questions that are of no interest for the community, directly.