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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