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First of all, I'd like to express my gratitude to the community for the support that keeps researchers like myself afloat! I have a unique set of oncology patients that I want to evaluate for brain atrophy in a retrospective longitudinal analysis. I was thinking about using Aseg.auto results to assess longitudinal volume changes, but before I invest all the time I wanted to check with the community whether this makes any sense at all: The dataset that looks like this: - 22 patients (no control dataset [yet]) - 10-25 MRIs per patient acquired over 2-8 years in relatively uniform intervals - Patients had most of their scans on the same scanner, but scanners differed widely between patients - All patients have axial T1 post gadolinium scans of 1x1x5mm resolution (3D acquisition available in <10%) - About 80% of scans have an axial pre-contrast T1 sequence - All scans are skullstripped (third party algorithm) I'm looking for crude changes, no subtleties; volumes of interest are: - Whole brain volume - White matter volume - Ventricular volume (mainly lateral ventricle) - Subcortical gray matter volume (whole thalamus most importantly) I ran a few test analyses and to my surprise I was able to generate pretty acceptable surfaces, however, topology fixing took about 24H per scan, and I feel aseg.auto contained all the volumetric data I was really interested in. My concrete questions are: 1) Does the full autorecon pipeline affect Aseg.auto? If there is no benefit, I could reduce the per scan analysis time from 28 hours to 1-2 h. 2) Would this low-resolution dataset be accepted by reviewers if used for Aseg? Should I do any quantitative validation beyond a visual quality analysis of Aseg? 3) Can I perform a longitudinal analysis only for the Aseg results? 4) Is it OK to use T1-gad images for the analysis? I'd appreciate any input! Best regards, David O. Kamson, MD PhD Neuro-oncology fellow Johns Hopkins Hospital & National Institutes of Health
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