End-to-end Cortical Surface Reconstruction from Clinical Magnetic Resonance Images
Journal:
arXiv
Published Date:
May 20, 2025
Abstract
Surface-based cortical analysis is valuable for a variety of neuroimaging
tasks, such as spatial normalization, parcellation, and gray matter (GM)
thickness estimation. However, most tools for estimating cortical surfaces work
exclusively on scans with at least 1 mm isotropic resolution and are tuned to a
specific magnetic resonance (MR) contrast, often T1-weighted (T1w). This
precludes application using most clinical MR scans, which are very
heterogeneous in terms of contrast and resolution. Here, we use synthetic
domain-randomized data to train the first neural network for explicit
estimation of cortical surfaces from scans of any contrast and resolution,
without retraining. Our method deforms a template mesh to the white matter (WM)
surface, which guarantees topological correctness. This mesh is further
deformed to estimate the GM surface. We compare our method to
recon-all-clinical (RAC), an implicit surface reconstruction method which is
currently the only other tool capable of processing heterogeneous clinical MR
scans, on ADNI and a large clinical dataset (n=1,332). We show a approximately
50 % reduction in cortical thickness error (from 0.50 to 0.24 mm) with respect
to RAC and better recovery of the aging-related cortical thinning patterns
detected by FreeSurfer on high-resolution T1w scans. Our method enables fast
and accurate surface reconstruction of clinical scans, allowing studies (1)
with sample sizes far beyond what is feasible in a research setting, and (2) of
clinical populations that are difficult to enroll in research studies. The code
is publicly available at https://github.com/simnibs/brainnet.
