  Supervised reconstruction models are characteristically trained on matched
pairs of undersampled and fully-sampled data to capture an MRI prior, along
with supervision regarding the imaging operator to enforce data consistency. To
reduce supervision requirements, the recent deep image prior framework instead
conjoins untrained MRI priors with the imaging operator during inference. Yet,
canonical convolutional architectures are suboptimal in capturing long-range
relationships, and priors based on randomly initialized networks may yield
suboptimal performance. To address these limitations, here we introduce a novel
unsupervised MRI reconstruction method based on zero-Shot Learned Adversarial
TransformERs (SLATER). SLATER embodies a deep adversarial network with
cross-attention transformers to map noise and latent variables onto
coil-combined MR images. During pre-training, this unconditional network learns
a high-quality MRI prior in an unsupervised generative modeling task. During
inference, a zero-shot reconstruction is then performed by incorporating the
imaging operator and optimizing the prior to maximize consistency to
undersampled data. Comprehensive experiments on brain MRI datasets clearly
demonstrate the superior performance of SLATER against state-of-the-art
unsupervised methods.
