Hi David,

I am not very optimistic:

5mm is too thick for FreeSurfer (recommendation is 1 up to 1.5). You will certainly get something, but it can be very unreliable and completely wrong. Especially longitudinally these thick slices will induce large variance due to different head positioning (and different slice angulations) in the scanner.

Furthermore, FreeSurfer does not take Gad-Enhanced images. Also it will not work if tumor lesions are present.

About your questions:

1. Surfaces update the aseg, but if you are only interested in the volumes, you can skip this expensive step (potentially at the cost of slightly higher noise levels in your measurements).

2. I think not (see above). 5mm is too low.

3. Theoretically yes, but I have never tested if the scripts will do it. You could run up to the aseg in the cross, then create base (up to aseg) and then run the longs up to aseg. Not even sure you really need the base aseg. You might be able to just run the initial base registration step, obtain the transformations and median norm.mgz image, could be sufficient for the long runs.

4. No. Gad images won't work.

Best, Martin

On Mon, 2019-10-07 at 18:12 +0000, David Kamson wrote:

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Freesurfers,

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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