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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:
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.