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Hello Matt, Thanks for the command line information. It's possible that the differences you are seeing by rerunning with the restored image is due to fast4's initial bias field estimation being different to that used in the -I iterations. Hence by running twice you are getting the initial correction applied twice ( on the original and restored images respectively). I would be very interested if you think that running fast4 twice always gives a better segmentation or if it's data set dependent. The --Hyper option is just a scaling factor ( set to < 0 to auto-estimate ) the --lowpass option is in mm. Many Regards Matthew > Hi Matthew, > > Thanks for your reply. Here is an example commandline applied to a > T1 image: > > fast4 -b -o default T1Struct > fslmaths T1Struct -mul default_bias T1Struct_default > fast4 -b -I 8 -o I8 T1Struct > fslmaths T1Struct -mul I8_bias T1Struct_I8 > fast4 -b -I 12 -o I12 T1Struct > fslmaths T1Struct -mul I12_bias T1Struct_I12 > > The files T1Struct_default T1Struct_I8 T1Struct_I12 are all the > same. If I run the default, and then run the default again on the > restored image I get a different (somewhat better) result. Also, > what are the units of the --lowpass and ---Hyper smoothing options? > > Thanks, > > Matt. > > From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On > Behalf Of Matthew Webster > Sent: Monday, April 07, 2008 6:39 AM > To: [log in to unmask] > Subject: Re: [FSL] Fast4 Question > > Hi, > We've done some tests with fast4 and we can't replicate what > you've seen with an invariant bias field for different --iter. Could > you go into more detail into the method you've used ( specific > command lines etc)? Given the smaller brain size you may want to > lower the --lowpass and ---Hyper smoothing options... > > Many Regards > > Matthew > > 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. >