I don't think it should be a problem to run on skull stripped data.
It is ok to run the default recon-all on the submillimeter data as it
will just downsample it to 1mm. But using the -hires option is good to
get any hires info. It does take a lot longer and sometimes it does
funny things, but if the final images and surfaces look ok, I think it
is good.
You might try version 8 on mp2rage (without skull stripping) as I think
it works well (but correct me if you find otherwise)
On 4/16/2025 8:54 AM, Sabrina Turker wrote:
External Email - Use Caution
Dear all,
I'm new to freesurfer and I'm trying to preprocess a 7T mp2rage
dataset (0.6mm³ isotropic, TR=6000ms, TE=2.05ms) with freesurfer 7.2.0.
The images have been corrected for B1, for intensity inhomogeneity
with ANTs N4 bias field removal and they were skull striped with FSL
based
on intensity. This was done bya colleague who analyzed this dataset
in 2017 and I'm hoping to run our updated toolboxes on the preprocessed
data. However, he ran into several problems while first analyzing the
data: freesurfer did not like the noisy background of the mp2rage,
which is why
he skullstripped the data before preprocessing it with freesurfer. He
then ran recon-all once with the default pipeline and once with skull
strip deactivated.
According to him, the default pipeline yielded better results. I'm
using his skull-stripped data now for the preprocessing and was
wondering whether not
deactivating the skull strip will lead to inaccurate measures for
cortical thickness and gray matter volume. Since this is crucial for
our toolboxes, we really
want to be sure that we're going for the best solution. A second
problem is the high resolution of the data - it's not recommended to
run the default recon-all
on anything lower than 1mm³ (*MailScanner has detected a possible
fraud attempt from "secure-web.cisco.com" claiming to be*
https://surfer.nmr.mgh.harvard.edu/fswiki/SubmillimeterRecon), but
most of the information I could find is from 2016 or 2017.
At the time, he used the -hires flag but got very bad results, which
made him use the default pipeline even if the mp2rage data got
downsampled. I was
wondering if anybody has feedback on how they analyzed a similar
dataset (high resolution, problems with skullstripping etc.), general
opinions on the best
way to analyze this dataset and/or if there have been any developments
(e.g., new flags for recon-all flags?) that I'm not aware of. We got
feedback from
one colleague that they did analyze their dataset successfully with
the -hires flag and did not encounter any problems at the time. Any
advice would be much
appreciated!
Best and thanks in advance,
Sabrina
--
Dr. Sabrina Turker (she/her)
Postdoctoral researcher
Brain and Language Lab
Department of Behavioral & Cognitive Biology
University of Vienna
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