Advancing Brain Morphometry at 7T: A Pilot Study on Epilepsy Patients

Motivation: Unlock the full potential of ultra-high field MRI enabling the detection of subtle structural changes. Goal(s): Investigate the performance of a Deep Learning (DL)-based brain segmentation algorithm for 7T MR Images. Approach: We trained a DL model for volume-based morphometry using T1-weighted brain images acquired at 7T. We then evaluated model performance on 7T scans for 80 epilepsy patients scanned at both 7T and 3T. Segmentations and volumetric estimates obtained from the patients' scans at both field strengths were compared qualitatively and quantitatively. Results: We observed consistent aging trends, minimal volumetric discrepancies, and comparable atrophy patterns across field strengths. Impact: This work introduces a novel and reliable brain morphometry algorithm that provides detailed structural insight for enhanced clinical decision support in epilepsy care.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/4286
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
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article

Advancing Brain Morphometry at 7T: A Pilot Study on Epilepsy Patients

Maxime O. Baud, Tom Hilbert, Martina Goeldlin, Kaspar A. Schindler et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Functional Brain Connectivity Studies
article

Advancing Brain Morphometry at 7T: A Pilot Study on Epilepsy Patients

Maxime O. Baud, Tom Hilbert, Martina Goeldlin, Kaspar A. Schindler, Punith Venkategowda, Patrick Alexander Liebig, Johannes Slotboom, Gabriele Bonanno, Robin Martin Heidemann, Bénédicte Maréchal, Gian Franco Piredda, Margitta Seeck, Piotr Radojewski, Robert Hoepner, Roland Wiest, Tommaso Di Noto, Serge Vulliémoz, Jean‐Philippe Thiran, David Julian Seiffge, Tobias Kober, K Prabhu, Lina Bacha
article en

Abstract

Motivation: Unlock the full potential of ultra-high field MRI enabling the detection of subtle structural changes. Goal(s): Investigate the performance of a Deep Learning (DL)-based brain segmentation algorithm for 7T MR Images. Approach: We trained a DL model for volume-based morphometry using T1-weighted brain images acquired at 7T. We then evaluated model performance on 7T scans for 80 epilepsy patients scanned at both 7T and 3T. Segmentations and volumetric estimates obtained from the patients' scans at both field strengths were compared qualitatively and quantitatively. Results: We observed consistent aging trends, minimal volumetric discrepancies, and comparable atrophy patterns across field strengths. Impact: This work introduces a novel and reliable brain morphometry algorithm that provides detailed structural insight for enhanced clinical decision support in epilepsy care.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 75%
Functional Brain Connectivity Studies
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