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Abstract #1978

Training- and Database-free Deep Non-Linear Inversion (DNLINV) for Highly Accelerated Parallel Imaging and Calibrationless PI&CS MR Imaging

Andrew Palmera Leynes1,2 and Peder E.Z. Larson1,2
1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, 2UC Berkeley - UC San Francisco Joint Graduate Program in Bioengineering, Berkeley and San Francisco, CA, United States

Deep learning-based MR image reconstruction can provide greater scan time reduction than previously possible. However, this is limited only to MR acquisitions that have large training datasets with fixed hardware and acquisition configurations. We introduce a training- and database-free deep MR image reconstruction technique that may unlock acceleration factors beyond the limits of current state-of-the-art reconstruction methods while being generalizable to any hardware and acquisition configuration. We demonstrate Deep Non-Linear Inversion (DNLINV) on different anatomies and sampling patterns and show high quality reconstructions at higher acceleration factors than previously achievable.

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