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 (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 (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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