VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction

TL;DR

VORTEX introduces physics-driven data augmentation techniques for MRI reconstruction, significantly enhancing robustness and efficiency by leveraging MRI physics and consistency training, outperforming existing methods especially in challenging, limited-data scenarios.

Contribution

The paper presents VORTEX, a novel physics-based data augmentation method that improves MRI reconstruction robustness and label efficiency, surpassing state-of-the-art image-based augmentation and self-supervised approaches.

Findings

Improves robustness to noise and motion artifacts.

Outperforms state-of-the-art augmentation techniques.

Enables combining heterogeneous augmentations.

Abstract

Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction. However, such models require a large number of fully-sampled ground truth datasets, which are difficult to curate, and are sensitive to distribution drifts. In this work, we propose applying physics-driven data augmentations for consistency training that leverage our domain knowledge of the forward MRI data acquisition process and MRI physics to achieve improved label efficiency and robustness to clinically-relevant distribution drifts. Our approach, termed VORTEX, (1) demonstrates strong improvements over supervised baselines with and without data augmentation in robustness to signal-to-noise ratio change and motion corruption in data-limited regimes; (2) considerably outperforms state-of-the-art purely image-based data augmentation techniques and self-supervised reconstruction methods on both in-distribution and out-of-distribution data; and (3) enables composing heterogeneous image-based and physics-driven data augmentations. Our code is available at https://github.com/ad12/meddlr.