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I have found that running fast4, restoring the image with the bias field, and then rerunning fast4 several times gives a better estimation of the bias field than just running fast4 a single time. I tried to duplicate this by increasing the number of main-loop iterations during bias-field removal (the --tier option) from 4 to 8, for example, but this gave exactly the same result for the bias field restoration. How would I get the same improvement in bias field estimation without rerunning the program several times (and wasting time calculating segmentations that I don't plan to use)? Also, are there any settings I should change for non-human primates with their smaller brains? It seems like sulci are often smoothed out of the segmented image. Thanks, Matt.