Hello everyone,
I am trying to perform a bias field correction on a 4D echo series that is used for quantitative MRI. There is significant B0 inhomogeneity which, on a single echo basis, I am able to correct for via the N4 filter. However, understandably, correcting each echo separately with N4 destroys the signal evolution and gives nonsensical quantitative values after calculation. I tried to normalize each corrected echo to their non-corrected counterpart inside a brain mask with different tools, this unfortunately didnt help.
Interestingly, applying the PatchBasedDenoising-Filter after the N4 correction for each echo separately seemed to correct this somehow, with quantitative values again being in a plausible range. I was able to replicate this by k-means clustering voxels together depending on their decay values over all echoes and then averaging the clusters inside each echo, which also somewhat restored the pre-correction value distribution. I assume that the N4 filter introduces some local, random error that is averaged out by the clustering, but I am not really sure about how this works exactly.
I would be very interested if anyone has figured out a way to perform bias field correction in 4D images, perhaps specifically in qMRI applications. I would also be thankful for any insights into why the PatchBasedDenoising and the clustering “fixes” my signal evolution.
Thanks!