The next thing I will like to do is to weight these matrices by (a) mean FA and to (b) normalise the streamline counts by the average volumes between 2 regions
Personally I advise against that; but to each their own.
dwipreproc -0 i_denoised.nii i_preproc.mif -rpe_none
Only now realised that you’re not correcting EPI susceptibility distortions. As discussed recently in another post, using ACT without having corrected these distortions is definitely going to result in tracking errors, since in areas of significant distortion you won’t be accessing diffusion and tissue data from the same anatomical location. Unfortunately I can’t give definitive advice on how to proceed if you don’t have the prerequisite data though.
Visualise 5TT
5tt2vis i_freesurfer_5TT.mif i_freesurfer_5TT_vis.mif
@rsmith: Does this look right? The white spots (intensity = 2) correspond to locations of lesions on the WM. How can I deal with them?
These come out due to the contents of file scripts/data/FreeSurfer2ACT.txt, which maps FreeSurfer segmentation indices to ACT 5TT tissue volumes. Currently, streamlines will not have any ACT priors applied to them while they traverse through these ‘pathological tissue’ regions. If you don’t want this behaviour, and want to instead label those voxels as white matter, you would do:
mrconvert i_freesurfer_5TT.mif -coord 3 4 -axes 0,1,2 -datatype bit - | 5ttedit - i_freesurfer_5TT_nopath.mif -wm -
I have also tried 5ttgen -fsl but the I am struggling to get good BETs for the 5TT with over-invasive cuts into the cortical GM…
Yes this is very annoying. I’ve set the bet parameter to be very relaxed and over-estimate the brain extent rather than under-estimate, but the precise behaviour is out of my control. Out of curiosity, are you getting the standard_space_roi failure warning? BET performs worse if this stage doesn’t do what it’s supposed to.
tckgen … -number 1M
tcksift … -term_number 500000
Reducing the number of streamlines by a factor of 2 is unlikely to fully correct the biases in streamlines reconstruction density. I would suggest taking a look at the raw plots in the original paper. With dynamic seeding the initial estimate is closer, so you might not need to remove quite as many streamlines as with WM or GM-WM interface seeding, but a factor of 2 is probably still inadequate. Alternatively you can try SIFT2; but poor alignment between DWI and 5TT due to EPI distortions may cause undesirable behaviour with the current SIFT2 solver (individual streamlines getting huge weights)...
> Specifically, should we very strict and expect a perfect brain from BET since it will be used for the 5TT and ACT? I keep getting a bit of skull left. From the FSL mailing list it was also recommended not to tweak the -f value for each case, and to keep it consistent across all subjects if possible.
Yes, I wouldn't be tweaking that value between subjects; manual refinement of the masks would be better than that. If it's really problematic, you can alternatively try using some other software to perform the brain extraction, and feed that to `5ttgen` using the `-mask` or `-premasked` options. And once you've decided on the best program to use, which has a command-line interface, let me know so that I can incorporate it into `5ttgen` and use it myself :stuck_out_tongue: