Processing of volumes prior to dwipreproc

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omit the denoising step from processing of just that set of volumes

What set of volumes are you referring to here?

This is in reference to whatever set of images are to be provided to FSL topup via the -se_epi option. But the wording is maybe clumsy; that’s me trying to avoid writing an essay in every thread :-/

Let’s suppose the situation is (as it sounds applies to your case), that you have:

  1. Phase encoding AP: 1 b=0, 60 b=3000 volumes;
  2. Phase encoding PA: 1 b=0.

, and the tools you have at your disposal are:

  • dwidenoise;
  • mrdegibbs;
  • dwifslpreproc;
  • All other MRtrix3 utilities.

The question is: What data do you provide to each command, and in what order?

  1. If you run all the phase encoding AP data through dwidenoise and mrdegibbs, then extract the solitary b=0 volume and concatenate it with the PA b=0 volume, then you have a situation where topup is estimating an inhomogeneity field where one volume has a different noise level than the other, and one has had Gibbs ringing removed whereas the other has not. This may cause issues, it may not.
    (You could also run the PA b=0 image through mrdegibbs here)

  2. If you concatenate the PA b=0 image with the AP volumes, then run the whole lot through dwidenoise | mrdebiggs, you have the advantage that the same processing has been applied to both volumes that topup will be utilising; the disadvantage is that noise level estimation may not be quite right for the PA volume if spatial distortions are large, and AFAIK nobody has pursued quantifying just how deleterious this may be.

  3. Extract the AP b=0 image as the first step, concatenate it with the PA b=0 image, and call it X; you then feed all AP data through dwidenoise | mrdegibbs, that forms the input image to dwifslpreproc; you could then feed X through mrdegibbs if you wished, but you can’t run dwidenoise on it as there are too few volumes, that’s then provided via the -se_epi option to dwifslpreproc. The input volumes to topup have now had identical processing applied to them, so there’s no known bias to speak of; but the level of noise in those images is higher than it may otherwise be.

I would say there is not currently a consensus on which of these options is preferable. Personally I’m moving away from acquisitions like this so it’s less of a concern… But for retrospective data the question has been raised a few times and I don’t think any evidence was presented for one approach over the others.

Rob

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