Just checking: are you wondering about a good threshold, or how to achieve this operation in practice? For the threshold: 50% is surely fine, anything beyond that is severely partial volumed with genuine CSF, I reckon. For completeness sake, note that the peer-reviewed (and revised) final paper of that pre-print is published over here. This is relevant for the complete data and results of the hippocampus.
To achieve this in practice, first make sure your hippocampal masks are correctly aligned to the subject (i.e. to the TW, TG and TC images). Then, also make sure these masks are regridded to those images, for convenience. In what follows, I’ll assume your
bhipp2diff.nii.gz is indeed already accurately aligned and regridded to these images; this is crucial of course, as it’s a small and somewhat intricately shaped region.
Then, compute a mask, from TC, of the voxels to exclude wrt the 0.5 threshold (you could also go for the opposite mask, and modify the other steps down the track accordingly of course):
mrthreshold TC.nii.gz too_much_TC.mif -abs 0.5
too_much_TC.mif looks like a mask of all voxels where you realistically expect TC > 0.5 (i.e. ventricles and such).
mrcalc to “remove” this mask from your
bhipp2diff.nii.gz. There’s many different ways of doing this, and different logic works intuitively for some people (but not others). Here’s just one way that’ll work. The key here is: your current
bhipp2diff.nii.gz generally has all voxels of interest, except for everything in
too_much_TC.mif, which you want “gone” from
bhipp2diff.nii.gz. So this one will do the trick:
mrcalc too_much_TC.mif 0 bhipp2diff.nii.gz -if cleaned_hipp_mask.mif
I’ve chosen to use the “if” operator in this example. So this command reads more or less like:
too_much_TC.mif, THEN set the voxel to 0, ELSE just use the existing value in
…and then the final thing is stored in
cleaned_hipp_mask.mif in the end. You should finally double-check that that one still looks like the hippocampus mask, but with the
too_much_TC.mif voxels removed (if any).