[Neuroimaging] Scrubbing DTI images

[Eleftherios Garyfallidis]

Hi Mahmoud,

What you need to do using RESTORE is find the dwi volumes which are
outliers (using the fit errors) and remove them from the data.

Does anyone has written a function for doing this automatically? If yes
please be happy to share it with the rest of us here and of course with
Mahmoud. Otherwise, Mahmoud you can try to do this by yourself by looking
at the tutorial given above but for this you will have to put some extra
effort to understand the different DIPY classes and functions. We are here
to help, so let us know how it goes.

Best,
Eleftherios



On Tue, May 17, 2016 at 5:09 PM Oscar Esteban <code at oscaresteban.es> wrote:

> Hi Mahmoud,
>
> Just to add some to Chris' response. RESTORE will fit the DTI rejecting
> outliers "voxel-wise".
>
> Probably you want first to run something like FSL Eddy (
> http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/EDDY/UsersGuide). Eddy will deal
> with head motion, eddy currents and susceptibility distortions. One
> approach to remove outliers is denoising, i.e. using
> http://nipy.org/dipy/examples_built/denoise_nlmeans.html
>
> If you do not denoise, RESTORE may a good option if you want to fit DTI.
>
> Cheers,
> Oscar
>
> On Tue, May 17, 2016 at 1:34 PM, Chris Filo Gorgolewski <
> krzysztof.gorgolewski at gmail.com> wrote:
>
>> You should look into RESTORE which fits DTI with outliers removed/down
>> weighted: http://nipy.org/dipy/examples_built/restore_dti.html
>>
>> On Mon, May 16, 2016 at 1:08 PM, Zeydabadinezhad, Mahmoud <
>> mahmoud.zeydabadinezhad at emory.edu> wrote:
>>
>>> Dear all,
>>>
>>> I have some DTI images collected in 61 directions + 6 B0s in 3T. I was
>>> wondering if you could give me a hint how can I do scrubbing on my DTI data
>>> either using FSL or DIPY or any other script.
>>> By scrubbing, I mean the same idea used in fMRI community i.e. detecting
>>> the outlier/motion corrupted slices or volumes and removing them from DTI
>>> calculation.
>>> I appreciate your time and help.
>>>
>>> Thank you!
>>> Mahmoud
>>>
>>>
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