<p dir="ltr">Denoising will give you back plausible values based on neighborhood information, it will not do whole volume rejection, restore will do that for you though based on the tensor model.</p>
<div class="gmail_quote">On May 18, 2016 8:53 AM, "Mahmoud" <<a href="mailto:zeydabadi@gmail.com">zeydabadi@gmail.com</a>> wrote:<br type="attribution"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Thank you all for the hints.<div><br></div><div>Oscar,</div><div>Could you elaborate more about what you suggested? I mean what exactly denoising is doing?</div><div><br></div><div>Thank you!</div><div>Mahmoud</div></div><div class="gmail_extra"><br><div class="gmail_quote">On Tue, May 17, 2016 at 4:43 PM, Oscar Esteban <span dir="ltr"><<a href="mailto:code@oscaresteban.es" target="_blank">code@oscaresteban.es</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi Mahmoud,<div><br></div><div>Just to add some to Chris' response. RESTORE will fit the DTI rejecting outliers "voxel-wise".</div><div><br></div><div>Probably you want first to run something like FSL Eddy (<a href="http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/EDDY/UsersGuide" target="_blank">http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/EDDY/UsersGuide</a>). Eddy will deal with head motion, eddy currents and susceptibility distortions. One approach to remove outliers is denoising, i.e. using <a href="http://nipy.org/dipy/examples_built/denoise_nlmeans.html" target="_blank">http://nipy.org/dipy/examples_built/denoise_nlmeans.html</a></div><div><br></div><div>If you do not denoise, RESTORE may a good option if you want to fit DTI.</div><div><br></div><div>Cheers,</div><div>Oscar</div></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">On Tue, May 17, 2016 at 1:34 PM, Chris Filo Gorgolewski <span dir="ltr"><<a href="mailto:krzysztof.gorgolewski@gmail.com" target="_blank">krzysztof.gorgolewski@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">You should look into RESTORE which fits DTI with outliers removed/down weighted: <a href="http://nipy.org/dipy/examples_built/restore_dti.html" target="_blank">http://nipy.org/dipy/examples_built/restore_dti.html</a></div><div class="gmail_extra"><br><div class="gmail_quote"><div><div>On Mon, May 16, 2016 at 1:08 PM, Zeydabadinezhad, Mahmoud <span dir="ltr"><<a href="mailto:mahmoud.zeydabadinezhad@emory.edu" target="_blank">mahmoud.zeydabadinezhad@emory.edu</a>></span> wrote:<br></div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div>
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Dear all,</div>
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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.</div>
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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.</div>
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I appreciate your time and help.</div>
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Thank you!</div>
<div style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:16px;margin-top:0px;margin-bottom:0px">
Mahmoud</div>
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