Zero-Shot Self-Supervised Learning for MRI Reconstruction

Deep learning (DL) has emerged as a powerful tool for accelerated MRI reconstruction, but these methods often necessitate a database of fully-sampled measurements for training. Recent self-supervised and unsupervised learning approaches enable training without fully-sampled data. However, a database of undersampled measurements may not be available in many scenarios, especially for scans involving contrast or recently developed translational acquisitions. Moreover, database-trained models may not generalize well when the unseen measurements differ in terms of sampling pattern, acceleration rate, SNR, image contrast, and anatomy. Such challenges necessitate a new methodology that can enable scan-specific DL MRI reconstruction without any external training datasets. In this work, we propose a zero-shot self-supervised learning approach to perform scan-specific accelerated MRI reconstruction to tackle these issues. The proposed approach splits available measurements for each scan into three disjoint sets. Two of these sets are used to enforce data consistency and define loss during training, while the last set is used to establish an early stopping criterion. In the presence of models pre-trained on a database with different image characteristics, we show that the proposed approach can be combined with transfer learning to further improve reconstruction quality.