Hi Mark, for some reason my reply did not include your previous answer, and it had some unusual characters in it...so I edited my text, and copied and pasted your previous answer below...
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Hi Mark,
Thanks so much for the great answer! As far as the answers to the first two questions I asked, I completely understand. I am just using BET to extract the brain, no need for betsurf, hence no need for T2-weighted scans.
As far as the proper non-brain-to-brain ratio after BET, here are some thoughts and more questions...and I apologize for the long email:
1. I just want to explicitly state that I'm performing a functional MRI experiment, and I took a T1 structural as well as a B0 fieldmap. I'm using the FEAT GUI to analyze functional MRI data, so I'm doing my registration there. If I understood things at the FSL course correctly, I'm registering my functional data to my BET'ed T1 with an affine transformation (6 DOF, FLIRT within FEAT), and I'm registering my BET'ed T1 to the MNI152 brain with a non-liner transformation (12 DOF, FNIRT within FEAT). Oh, and I'll also be use the B0 fieldmap in FUGUE within FEAT to correct for bias. Is this correct?
2. Just clarify the problem; it's my concern over the non-brain-to-brain ratio after BET. It's not localized to one specific area (like the frontal cortex), but rather wherever there is a dura-to-brain transition. For example, depending on the parameters I've entered, after BET'ing I am never left with a 90% dura-free brain without having parts of cortex removed as well. So I need to settle for a 65-70% dura-free brain in order for the cortex to appear 98% intact. I'm just trying to find the right non-brain-to-brain ratio. Am I to understand that you think this ratio is relatively unimportant when doing FLIRT or FNIRT (in FEAT as I am doing it)? I mean, a little extra dura here, a little more brain over there...doesn't make a big difference because of the way the brain is transformed?
3. Also, do the above-mentioned problems with BET (the non-brain-to-brain ratio when just looking at a brain post-BET, not checking registration, etc) imply that I have a field bias? My T1 structural scan looks uniformly bright, but how do I know? To note, I'm consistently having to provide a slightly negative value for the "threshold gradient" (around -0.1) in order to get the neck area (what is left after ROI'ing) removed. Does this mean my field has a bias? If so, you mentioned to use FAST, but I'm not sure how to use this, and also, if I'm not mistaken, I thought this was something that was done in FUGUE (or FUGUE within FEAT) using a B0 field map after I BET. Am I correct? Sorry, just got a little confused because I am unfamiliar with FAST :)
4. Final question...you mentioned that FNIRT uses the whole head structural scan for the last few passes...does FEAT know to do this? I don't see where in the FEAT GUI I can enter the whole head T1 scan. If this is a problem, should I be doing all my registration outside of FEAT?
Thanks so much! I really appreciate your time and effort.
Have a great day,
Dar
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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
>
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