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Abstract #1050

Longitudinal FreeSurfer with non-linear subject-specific template improves sensitivity to cortical thinning

Malte Hoffmann1,2, David Salat1,2, Martin Reuter*1,2,3, and Bruce Fischl*1,2,4
1Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3German Center for Neurodegenerative Diseases, Bonn, Germany, 4Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States

Longitudinal FreeSurfer creates a within-subject template by rigidly registering and median-filtering longitudinal timepoints (TP). Information common to all TPs is extracted from the template for unbiased TP initialization, resulting in substantial improvements over cross-sectional processing. However, this approach is not optimal in the presence of severe atrophy or other large-scale anatomical change, which causes voxels to be filtered across tissue classes. We address this problem by introducing an enhanced longitudinal stream that deforms each TP using non-linear registration to construct the template. We demonstrate considerable increases in sensitivity to cortical thinning, without affecting test-retest reliability.

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