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Mean Signal DKI #1230
In this PR you can find the implementation of the mean spherical DKI model. This model provides non-Gaussian indexes that do not depend on the orientation distribution fiber and that are less sensitive to artefacts when compared to standard mean kurtosis.
The missing parts to complete this PR are the following:
Please let me know if you have any additional comments that you want me to address at this point!
@@ Coverage Diff @@ ## master #1230 +/- ## ========================================= Coverage ? 83.84% ========================================= Files ? 120 Lines ? 14561 Branches ? 2294 ========================================= Hits ? 12209 Misses ? 1827 Partials ? 525
Hello! Apologies - progress on this was much slower than I've expected. Basically, I've decided to push back on this to double check if recent advances on microstructural modelling were making MSDKI an obsolete and useless technique. However, according to my recent study (Henriques et al., 2019), I can concluded that this type of signal representation approaches are still a valuable tool for the scientific community. Actually, I feel that this is one of the most exciting techniques that I am sharing in an open source environment. Hope you find it interesting as well.
As I've promised you, I've created an example where I've described be relevance of this technique and showed how one can reconstruct diffusion-weighted data using MSDKI. Hope you enjoy.
In my side this PR is ready to go, but let me know if you have further comments.
The tests might not be passing because I am having an intermittent error in dipy.workflows.tests.test_tracking.test_det_track (not related to this PR) in the environment with python 3.5. Should I create a issue on this?
Doesn't seem to be (for example: https://ci.appveyor.com/project/skoudoro/dipy-5dd9b/builds/21721304 from a PR I have in review), but maybe put up an issue about it, and we can try to resolve that together. It might be something intermittent. I agree that it seems unrelated to your changes, so it's a bit puzzling.
skoudoro left a comment
arokem left a comment
Great. I think this is really close to ready to merge. I had just a couple of small comments.
In particular, I wonder whether something could be done to speed up the prediction from this model. Right now, you are looping through voxels (here:
At the very least, could we use a mask for this function, to avoid predicting for voxels with no meaningful signal?