End-to-end Cortical Surface Reconstruction from Clinical Magnetic Resonance Images Article Swipe
Jesper D. Nielsen
,
Karthik Gopinath
,
Andrew Hoopes
,
Adrian V. Dalca
,
Colin Magdamo
,
S. R. Arnold
,
Sudeshna Das
,
Axel Thielscher
,
Juan Eugenio Iglesias
,
Oula Puonti
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.14017
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.14017
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.
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- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2505.14017
- https://arxiv.org/pdf/2505.14017
- OA Status
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- OpenAlex ID
- https://openalex.org/W4415021721
All OpenAlex metadata
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https://openalex.org/W4415021721Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2505.14017Digital Object Identifier
- Title
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End-to-end Cortical Surface Reconstruction from Clinical Magnetic Resonance ImagesWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-05-20Full publication date if available
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Jesper D. Nielsen, Karthik Gopinath, Andrew Hoopes, Adrian V. Dalca, Colin Magdamo, S. R. Arnold, Sudeshna Das, Axel Thielscher, Juan Eugenio Iglesias, Oula PuontiList of authors in order
- Landing page
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https://arxiv.org/abs/2505.14017Publisher landing page
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https://arxiv.org/pdf/2505.14017Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2505.14017Direct OA link when available
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0Total citation count in OpenAlex
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