Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers. (arXiv:2105.08059v1 [eess.IV])

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.

Source: https://arxiv.org/abs/2105.08059


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