Abstract #3115

Self-Supervised Image Reconstruction of 7T MP2RAGE for Multiple Sclerosis: 0.5mm Isotropic Resolution in 10 Minutes

Thomas Yu^1,2,3, Francesco La Rosa⁴, Gian Franco Piredda^1,5,6, Jonadab Dos Santos Silva⁴, Faye Bourie⁴, Henry Dieckhaus⁷, Govind Nair⁷, Patrick Liebig⁸, Jean-Philippe Thiran^2,3,5, Tobias Kober^1,2,3, Erin Beck^4,7, and Tom Hilbert^1,2,3

¹Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland, ²Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ³Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ⁴Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ⁵Centre d’Imagerie Biomédicale (CIBM), EPFL, Lausanne, Switzerland, ⁶Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland, ⁷National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States, ⁸Siemens Healthineers International AG, Erlangen, Germany

Synopsis

Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, Ultra-High Field MRIHigh-resolution 3D MR imaging is necessary for the detailed assessment of focal pathologies, such as cortical lesions. However, high-resolution demands tradeoffs with acceleration and SNR, which is difficult to address with standard machine learning reconstructions due to the infeasibility of collecting large datasets of fully sampled data. Using a dataset of high-resolution (0.5mm isotropic), 3D, 7T MP2RAGE scans of multiple sclerosis patients, we show that a self-supervised reconstruction from one scan, requiring no fully sampled data, has higher apparent SNR than a median of three scans, currently used for assessment, with comparable tissue contrast and lesion conspicuity.

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