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Hi, The T2-weighted scan and "BET2" in this context is only concerned with when you want to fit the "inner skull, outer skull and outer scalp" as it says in the slide. If all you want to do is extract the brain (from non-brain) then a single T1-weighted image is fine and is the most common case. You can't do the inner skull, outer skull and outer scalp estimation with the BET GUI - you need to run betsurf from the command line. As for large/small errors in BET. It is difficult to give any general rule of thumb because it depends on where the problem is and whether it is scatter or consistently in one area. Are the problems you are having related to bias field by any chance? You might benefit from running FAST to do bias correction (which does not need the initial BET result to be very accurate, as you will just use the bias field output which is very smooth and hence not unduly affected by small extra/missing regions). As for other tools - FLIRT is relatively insensitive because it only estimates 6 or 12 parameters from the whole brain, so as long as the erroneous parts make up a sufficient small region wrt the entire brain then it is unlikely to affect things a lot unless there is a very consistent change (like eroding the whole frontal part of the brain away by a few voxels - something like this would definitely cause problems). For FNIRT it doesn't matter as it uses the non-brain extracted images for the final passes and hence should recover from any small errors introduced before. The main one where there is a big effect is FAST when it is run for the purpose of extracting quantitative tissue volumes. In this case either missing parts of the brain or extraneous non-brain material *will* affect the volume estimates, normally enough to make the measurements not very useful (since it is typical to look for small changes in volume, and so small errors matter). In general, just try your best result and see what happens. If the registration looks fine then you don't have to worry. If you are doing something else, then check those results. Unless it is a difficult thing to check visually, then it normally suffices to *LOOK AT YOUR RESULTS*! :) All the best, Mark On 19 Jul 2011, at 12:55, Dar Meshi wrote: > Hi Everyone, > > I was at the latest FSL course in Montreal last month (thanks so much for the great course!) and I was just reviewing the slides on BET2. There is a slide on page 57 of the booklet which mentions BET2 "subsequently fits inner skull, outer skull and outer scalp using T1- and T2- weighted images." This leads me to ask a couple questions: > > 1. I actually didn't collect a T2-weighted structural scan, but I think FSL is performing BET as it should (see below). Should I be concerned that I don't have a T2-weighted scan? How does BET2 work without the T2-weighted scan? > > 2. In the future, if I was to collect a T2-weighted scan, how can I tell FSL which image file to use? Currently, I cannot find a way to link another input file in the BET GUI. > > Also, I've been having problems with BET either leaving too many pieces of non-brain, or taking big pieces of brain out....very hard to find the sweet spot with BET, and this changes with each brain I analyze. On page 56 of the booklet you show an example where "leaving small pieces of non-brain is unimportant for most apps". Could you please describe why this is? If I'm registering functional data to this T1 brain and also registering this T1 brain to the MNI standard, wouldn't any extra pieces, or brain deformities cause problems? Oh, and also could you possibly give an example of a tolerable amount of non-brain pieces? In the picture in the booklet you have 2 small pieces, but I'm having problems getting it down to such a small amount without taking big chunks out...so how much is too much? :) > > Thanks so much for your help! > > Have a great day, > Dar >