Abstract #2155
Automated brain morphometry for sub-millimeter 7T MRI using transfer learning
Gian Franco Piredda1,2,3, Punith B. Venkategowda4, Piotr Radojewski5,6, Tom Hilbert1,2,3, Arun Joseph6,7,8, Gabriele Bonanno6,7,8, Roland Wiest5,6, Karl Egger9, Shan Yang9, Jean-Philippe Thiran2,3, Ricardo A. Corredor-Jerez1,2,3, Bénédicte Maréchal1,2,3, and Tobias Kober1,2,3
1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 3LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Siemens Healthcare Pvt. Ltd., Bangalore, India, 5Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University, Bern, Switzerland, 6Translational Imaging Center, sitem-insel AG, Bern, Switzerland, 7Advanced Clinical Imaging Technology, Siemens Healthcare AG, Bern, Switzerland, 8Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland, 9Department of Neuroradiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
Synopsis
Large spatial signal variations due to field inhomogeneities complicate the application of automated brain morphometry at 7T. In this work, we propose to use transfer learning to adapt a template-based segmentation algorithm to sub-millimeter ultra-high field applications. More specifically, a convolutional neural network pre-trained on T1-weighted scans to extract the total intracranial volume (TIV) from MP-RAGE acquisitions was re-trained to retrieve the TIV mask directly from MP2RAGE volumes. The developed method proved to reliably deliver brain tissue masks and volumetry at 7T.
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