Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers

• URL: http://arxiv.org/abs/2105.08059v1
• Date: Sat, 15 May 2021 02:01:21 GMT
• Title: Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
• Authors: Yilmaz Korkmaz, Salman UH Dar, Mahmut Yurt, Muzaffer \"Ozbey, Tolga \c{C}ukur
• Abstract summary: We introduce a novel unsupervised MRI reconstruction method based on zero-Shot Learned Adrial TransformERs (SLATER) A zero-shot reconstruction is performed on undersampled test data, where inference is performed by optimizing network parameters. Experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against several state-of-the-art unsupervised methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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