Abstract
Supervised deep learning has swiftly become a workhorse for accelerated MRI in recent years, offering state-of-the-art performance in image reconstruction from undersampled acquisitions. Training deep supervised models requires large datasets of undersampled and fully-sampled acquisitions typically from a matching set of subjects. Given scarce access to large medical datasets, this limitation has sparked interest in unsupervised methods that reduce reliance on fully-sampled ground-truth data. A common framework is based on the deep image prior, where network-driven regularization is enforced directly during inference on undersampled acquisitions. Yet, canonical convolutional architectures are suboptimal in capturing long-range relationships, and randomly initialized networks may hamper convergence. 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 transformer blocks to map noise and latent variables onto MR images. This unconditional network learns a high-quality MRI prior in a self-supervised encoding task. A zero-shot reconstruction is performed on undersampled test data, where inference is performed by optimizing network parameters, latent and noise variables to ensure maximal consistency to multi-coil MRI data. Comprehensive experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against several state-of-the-art unsupervised methods.
Abstract (translated)
URL
https://arxiv.org/abs/2105.08059
