(ISMRM 2024) Supervised Pretraining and Self-Supervised Finetuning enables Robust Reconstruction of High-Resolution 7T MP2RAGE for Multiple Sclerosis
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Abstract #2820

Supervised Pretraining and Self-Supervised Finetuning enables Robust Reconstruction of High-Resolution 7T MP2RAGE for Multiple Sclerosis

Thomas Yu1,2,3, Francesco La Rosa4, Gian Franco Piredda1,5, Marcel Dominik Nickel6, Jonadab Dos Santos Silva4, Henry Dieckhaus7, Govind Nair7, Patrick Liebig6, Jean-Philippe Thiran2,3,8, Tobias Kober1,2,3, Erin Beck4, and Tom Hilbert1,2,3
1Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland, 2Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland, 3LTS5, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland, 4Icahn School of Medicine at Mount Sinai, New York, NY, United States, 5CIBM Center for Biomedical Imaging, Geneva, Switzerland, 6Siemens Healthcare GmbH, Erlangen, Germany, 7National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States, 8CIBM Center for Biomedical Imaging, Lausanne, Switzerland

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, Neuro, Multiple Sclerosis, Reconstruction

Motivation: The infeasibility to collect large datasets of high-resolution, fully-sampled 7T data hinders the training of deep learning reconstructions for accelerated high-resolution 7T scans.

Goal(s): Demonstrate that combining pretraining on fully-sampled scans from 1.5/3T and self-supervised finetuning on a few undersampled 7T scans enables robust reconstruction.

Approach: High-resolution(0.5mm isotropic), 3D, 7T MP2RAGE scans of multiple sclerosis patients are used for finetuning/testing.

Results: Deep learning reconstructions from one 7T scan have higher apparent SNR than a median of GRAPPA reconstructions from three scans, currently used for assessment, with similar tissue contrast and lesion conspicuity, potentially reducing scan time by a factor of three.

Impact: Very high-resolution 3D scans can require infeasible acquisition time to generate images suitable for clinical use. Our work shows the potential for deep learning reconstructions to reduce scan time by a factor of three, without fully-sampled 7T data.

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Keywords

reconstructiongrappamedianresolutioncontrastsupervisedlearning