Hi Octavian,
re influence of PE direction on noise, if any: should one apply denoising to each set separately then combine outputs, or combine both sets into a dwi.mif input then run
dwidenoise?
Intuitively, concatenating the data feels like a better approach. However I do vaguely recall that there was some discussion around issues with the fitting of the MP distribution, where we were encountering problems if the processing kernel contained too much data (either from a large spatial window, or a large number of volumes). But I don’t recall whether it was a fundamental issue, a software bug, or something else; hopefully @dchristiaens or @jdtournier can fill in.
as far as I know, fsl’s
topupalso corrects for intervolume motion for the b=0 images selected for distortion correction.
Correct. It does however assume that the susceptibility field remains constant despite that motion, which is unfortunately not the case, but there’s not a whole lot we can do about that right now.
Still, one could use both sets combined (dwi.mif) as input to
dwipreprocwith-rpe_all, orrpe_headerflags, or use a mean b=0 (lowb.mif, 10x PA, 10XAP, resulted withdwiextract) for distortion correction …
If you do not manually define the b=0 images to be used for inhomogeneity field estimation using the -se_epi option, the script will itself execute dwiextract in order to access those volumes from the input DWI. So the outcome between these two approaches should be equivalent.
… or more, use the 2-3 b=0 volumes in each set acquired closest in time to the dwi images (say lowb_3.mif, im.8,9,10 from 1st set, and 8,9,10 from 2nd set) as advised at some point.
Using those b=0 images acquired closest in time to the DWIs is recommended not precisely due to being closest in time, but because rotation of the head between those volumes is likely to be smallest (and hence variation in the susceptibility field itself is minimised). But generally the b=0 images are interspersed between all of the DWIs, and hence no b=0 volume is particularly closer to “the DWIs” than any other, so you might as well use all of them. The primary motivation for that particular comment I think is that if you were to acquire a reversed phase-encode b=0 pair, then your DWIs, then 2 hours of other sequences, then finally another reversed phase-encode b=0 pair, you’d be better off discarding that final pair and only using the first pair.
Any preferences, and if so, what would be a simple quality check (including eyeballing the
dwipreprocoutput)?
Can’t really make any life-changing revelations here, other than to use the -nocleanup option and have a look at the intermediary images.
Could one use the same argument and transform dwi (after
topup/eddy/dwipreproc) to T1 space, while keeping images at their original resolution, any issue with that (again, registration would here follow any motion/distortion correction)?
Only issue is that it requires reorientation of the diffusion gradient vectors to account for the rotation component of the transformation being applied. mrtransform should now perform this rotation seamlessly when applying an estimated transform to a DWI series, making it easier than it used to be.
b=0 target image in registration, does this benefit from preparation, I have seen code examples like this (BIDS mrtrix connectome), but this is variably applied:
That’s a very specific way of preparing for the rigid-body registration, which is based on this method. I admittedly have not done extensive testing on the approach, nor on the performance of mrregister with images constructed in this way. Only comment I’ll throw in is that both b=0 and T1 images should ideally be B1 bias field corrected before applying this technique. Eventually this method will likely become a common approach to DWI-T1 registration, but it’s not yet available.
re: dilation for
dwi2fod, I have seen 1 voxel (2 mm here) dilations in papers, is this what you recommend, if so how would you select n
Depends on your application. When using ACT, I tend to apply a heavy dilation to guarantee that no valid streamline position in the 5TT image is devoid of diffusion model data. Indeed in this use case, providing a mask for dwi2fod isn’t really necessary at all, it’s just decreasing execution time slightly by omitting definite non-brain voxels, but false positives are far less detrimental than false negatives in such a mask. Without ACT, the extent of that mask can indeed influence the extent of streamlines propagation; I can’t really provide any advice there.
Rob