Abstract #4286
Advancing Brain Morphometry at 7T: A Pilot Study on Epilepsy Patients
Lina Bacha1,2,3, Tommaso Di Noto1,2,3, Punith B. Venkategowda4,5, Keerthi Prabhu4, Gian Franco Piredda1, Gabriele Bonanno1,6,7, Johannes Slotboom8, David Seiffge9, Martina Goeldlin9, Robert Hoepner9, Serge Vulliemoz10,11, Margitta Seeck10,11, Kaspar Schindler9, Maxime Baud9, Jean-Philippe Thiran2,3, Tobias Kober12, Tom Hilbert1,2,3, Patrick A. Liebig12, Robin Heidemann12, Roland Wiest6,8, Piotr Radojewski6,8, and Bénédicte Maréchal1,2,3
1Advanced Clinical Imagning Technology, Siemens Healthineers International AG, Lausanne, Switzerland, 2LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 3Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 4Siemens Healthcare Pvt. Ltd., Bangalore, India, 5International Institute of Information Technology, Bangalore, India, 6Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland, 7Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland, 8Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland, 9Department of Neurology, University Hospital Bern, Inselspital University of Bern, Bern, Switzerland, 10EEG and Epilepsy Unit, Geneva University Hospitals and University of Geneva, Geneva, Switzerland, 11Center of Biomedical Imaging (CIBM), Geneva, Switzerland, 12Siemens Healthineers AG, Erlangen, Germany
Synopsis
Keywords: Analysis/Processing, Segmentation, Software Tools, Diagnosis/Prediction, Ultra-high-field MRI
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.
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