Abstract #2820

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

Thomas Yu^1,2,3, Francesco La Rosa⁴, Gian Franco Piredda^1,5, Marcel Dominik Nickel⁶, Jonadab Dos Santos Silva⁴, Henry Dieckhaus⁷, Govind Nair⁷, Patrick Liebig⁶, Jean-Philippe Thiran^2,3,8, Tobias Kober^1,2,3, Erin Beck⁴, and Tom Hilbert^1,2,3

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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.