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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