  Deep learning (DL) has emerged as a powerful tool for accelerated MRI
reconstruction, but often necessitates 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 translational acquisitions in development. Moreover,
recent studies show that 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
to enable subject-specific DL MRI reconstruction without external training
datasets, since it is clinically imperative to provide high-quality
reconstructions that can be used to identify lesions/disease for \emph{every
individual}. In this work, we propose a zero-shot self-supervised learning
approach to perform subject-specific accelerated DL MRI reconstruction to
tackle these issues. The proposed approach partitions the available
measurements from a single scan into three disjoint sets. Two of these sets are
used to enforce data consistency and define loss during training for
self-supervision, while the last set serves to self-validate, establishing 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 for faster convergence time and reduced
computational complexity. The code is available at
\url{https://github.com/byaman14/ZS-SSL}.
