Dear Bruce and Freesurfer Experts,

Thanks for the quick answer, Bruce, to previous post.

As I work my way through all the checks on output I had a couple of other quick questions

1) I noticed by skull-stripping was poor (too aggerssive at the back of the brain with part of the occipital lobe and a small part of cerebellum).  I therefore tried a number of suggestions from the tutorials including using -wsthresh 35 and -no-wsgcaatlas - this worked well but could someone explain why the lack of atlas use improves things ?

2) I also tried to improve skull stripping using the talairach_with_skull_2.lta option but I did not have this file despite recon-all -all having run fine the first time around with no errors/problems reported - should this concern me ?

3) I am using Centos 64 on a Linux box with 4 cores and 12 GB of RAM - how many instances of recon-all could I have running at the same time with no problems

Thank you.

Mo

On Thu, Nov 24, 2011 at 4:01 PM, Bruce Fischl <fischl@nmr.mgh.harvard.edu> wrote:
Hi Mo,

that's not a problem. We have different normalizations for different pieces of the recon. For subcortical we use the norm.mgz, which should *not* be eroding borders of thalamus, pallidum, etc.... The brain*.mgz are for cortex, where we don't care about those borders.

cheers
Bruce

On Thu, 24 Nov 2011, Mahinda Yogarajah wrote:

Dear Experts,

I am a beginner to the use of Freesurfer and having just installed version
5.1.0 (Centos 64bit) I have been going through the processing pipeline with
one of our own datasets (3T GE Excite II scanner - coronal T1-weighted
volumetric acquisition sequence with 1.1-mm thick slices).  recon-all works
fine and I have been looking at the outputs.

I have a question regarding the subcortical segmentation especially around
the basal ganglia - during the intensity normalisation it appears to have
assigned similar high (110) voxel values to parts of the internal capsule,
pallidum and thalamus (pic1- brainmask.mgz), though the actual segmentation
(pic2-aseg) looks reasonable when compared with the native image (pic3-raw
T1) (though I appreciate one is normalised and the other is not).  Should I
be worried about this (and if so how can I correct ?) or is this intensity
normalisation used only in the surface stream with a different intensity
normalisation used in the volume stream ?  Or ... is it used in the volume
stream but the other parts of the model (eg priors from atlas etc)
compensate in some way.

Thanks.

Mo

PS I can post screen




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