DiffusionImaging

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MITK Diffusion

Quicklinks

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 Medical Image Computing at the German Cancer Research Center (DKFZ).


Features & Highlights

  • Support for most established image formats, such as DICOM, NIFTI, NRRD, fib/vtk, tck and trk.
  • Image preprocessing
    • Registration
    • Head-motion correction
    • Denoising
    • Skull stripping and brain mask segmentation
    • Resampling, cropping, flipping and merging
    • Header modifications
    • Single volume extraction
  • Diffusion gradient/b-value processing:
    • b-value rounding
    • Gradient direction flipping
    • Gradient direction subsampling
    • Averaging of gradient directions/volumes
    • Gradient direction and b-value visualization
  • Tensor and Q-ball reconstruction, as well as other reconstructions via Dipy wrapping (CSD, 3D SHORE, SFM)
  • ODF peak extraction
  • Quantification of diffusion-weighted images:
    • Intravoxel Incoherent Motion (IVIM) and diffusion kurtosis analysis
    • Calculation many other derived indices such as ADC, MD, GFA, FA, RA, AD, RD
  • Fiber tractography and fiber processing
    • Global tractography [3]
    • Interactive (similar to [4]) deterministic and probabilistic peak, ODF and tensor streamline tractography with and without various possibilities for anatomical constraints.
    • Machine learning based tractography [5]
  • Brain network statistics and visualization (connectomics)
  • Fiberfox: simulation of diffusion-weighted MR images [6]
  • Fiber clustering [7]
  • Fiber fitting and weighting similar to SIFT2 and LiFE [8,9]
  • Direct white matter bundle segmentation (TractSeg) [10]
  • Interactive Python console
  • Command line tools for most functionalities

Since the last release 2017.07 there have been a lot of feature additions, bug fixes and


Downloads

If you encounter any bugs, please report them in our bugtracking system or use the MITK-users mailing list. We are grateful for any feedback!


Latest stable installers (2017.07)

Commit hash 2bda849ee86a362583ee3d2beb4baaca038bd8a5

Windows 7, Windows 10 MS Windows (64 bit) installer
Windows 7, Windows 10 MS Windows (64 bit) zip archive
Ubuntu 16.04 Ubuntu (64 bit), tar.gz archive
Ubuntu 14.04 Ubuntu (64 bit), tar.gz archive

Known issues fixed in the current master: The Fiberfox command line application does not read the b-value but uses a default b-value of 1000 s/mm². This bug does not affect the GUI version of Fiberfox. This bug also has no effect if the first non-zero b-value is 1000 s/mm², which is for example the case in the simulated HCP dataset (10.5281/zenodo.572345). This bug is fixed in the current master of the MITK source code.


Requirements

  • For Windows users:


Building MITK Diffusion from source

  1. Install Qt on your system.
  2. Clone MITK from out git repository using Git version control.
  3. Configure the MITK Superbuild using CMake.
    1. Choose the source code directory and an empty binary directory.
    2. Click "Configure".
    3. Set the option MITK_BUILD_CONFIGURATION to "DiffusionRelease".
    4. Click "Generate".
  4. Build the project
    1. Linux: Open a console window, navigate to the build folder and type "make -j8" (optionally supply the number threads to be used for a parallel build qith -j).
    2. Windows (requires visual studio): Open the MITK Superbuild solution file and build all projects.
  5. The build may take some time and should yield the binaries in "your_build_folder/MITK-build/bin"

More detailed build instructions can be found in the documentation.


References

[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] 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.

[4] 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.

[5] 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.

[6] 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.


Contact

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.

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