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.