Hi Philip,
The deformable template was most beneficial when differences between time
points (TPs) were largest. While this is perhaps not surprising, it meant that
the long registration time of 8h/TP in the abstract was not time well spent
since we were already certain of group differences.
We generally recommend the longitudinal stream for longitudinal data since much
information is shared between the TPs. The median rigid template is typically a
robust starting point for the processing of the individual TPs, sometimes even
for developmental studies where you see the size of the brain increase.
In your specific case, non-linear registration (using an image-intensity
objective like in the abstract) might be detrimental. The algorithm will likely
move tissue from outside the resected area into this area since the scan before
resection will have matching intensities there, but this won’t necessarily make
for a good template.
Malte
________________________________________
From: freesurfer-boun...@nmr.mgh.harvard.edu
<freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of Philip P
<philip.pruck...@gmail.com>
Sent: Sunday, February 4, 2024 01:48
To: freesurfer@nmr.mgh.harvard.edu
Subject: [Freesurfer] Longitudinal Freesurfer in the presence of large tissue
defects
External Email - Use Caution
Dear Freesurfer Developers,
I am currently running the Longitudinal Pipeline on a cohort with significant
portions of brain tissue removed. The obtained base templates overall look
reasonable, however, when overlaying the transformed session T1ws and flicking
between them, there still are noticeable residual differences. This is not
surprising, given the inevitable brain shift after tissue removal, which rigid
registration cannot account for. In my search for a non-linear base template
option I came across the "Longitudinal FreeSurfer with non-linear
subject-specific template improves sensitivity to cortical thinning” ISMRM
abstract (MailScanner has detected a possible fraud attempt from
"secure-web.cisco.com" claiming to be
https://archive.ismrm.org/2020/1050.html<https://secure-web.cisco.com/1me4RY0enlek75e3PAqIseSGOO7tfF84eBwPlWXYndHKO5G2tJADVy-TNvBizJR-LC2_sMvVChG7l5Pa8fM2tIAiP3DQT-SSQTQqRMUoLjOtjqu6MziPDQmMNRY5acq-T2Ufgp-DfTUhXPmCjLOm0i9Su5AxbRJ9lgN0NROMe0D3NZQJ0s72k_2YyzwWh9hncuI8MPyo0OJm0D5IdIvd_8-X2QAvcS54AE_ctHqeQi5RdtlySv2EfrrbKcuNkH5npl63jHoUptIM9QgGYk0XMO4RsHZYRXlfQW1IXJ1n62rfuX8xXiSbcBFppSYPx0utW1SkV7zhUcfnHb0qhtJwUFw/https%3A%2F%2Farchive.ismrm.org%2F2020%2F1050.html>)
as well as related code on Github (MailScanner has detected a possible fraud
attempt from "secure-web.cisco.com" claiming to be
https://github.com/mu40/freesurfer/tree/nf-long-nonlin<https://secure-web.cisco.com/10WRpCPnvc4D9H2TOaZHsTUn0u7cWtVKoReU9EtexFnzvs4ApXqcGX5LXIAqA1gw_jDyZM0n74IRnrQrlh-uC5HiUAB3QqdEQ4zL9c8zhF_OLGSzGLytRBv-qE6DlzxoJtfknjZSMHEk9AVKlDkNKUwBjYFIOBfFv0DA9Yeb5gqDUcqqZAprEL025Pp2xpCpTM3ZS5Qm_zuM2FG1j0boLUTmrLOeHCk3ls6LB6hI03mwTM2feKe8q6B2xiqJvcLmC8dKpj0jL8EEzZNi8PpkbFflk3RYfghezpSY7yOXY-dgVEzNbcZOVNd4lNmn-aZBLK1zqNqtODLsPG0JOP_Q3wA/https%3A%2F%2Fgithub.com%2Fmu40%2Ffreesurfer%2Ftree%2Fnf-long-nonlin>).
I was wondering whether you could provide insights into what you think the best
strategy for obtaining reliable longitudinal cortical thickness measurements in
such a dataset. Do you have a feel for whether the Longitudinal Freesurfer
processing stream still provides benefits over Cross-Sectional analysis in the
presence of large defects? Would you recommend giving the above-mentioned
non-linear implementation a shot or were there other reasons the feature has
not been implemented yet in the official version?
Thanks already in advance!
Best
Philip
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