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

arXiv: https://arxiv.org/abs/2111.02549 · HTML: https://mlsanity.com/paper/2111.02549/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2021.

## Built on by (most cited first, 16 of 16)

- [Monarch: Expressive Structured Matrices for Efficient and Accurate Training](https://mlsanity.com/paper/2204.00595/) (2022)
- [DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models](https://mlsanity.com/paper/2302.03018/) (2023)
- [S4ND: Modeling Images and Videos as Multidimensional Signals Using State Spaces](https://mlsanity.com/paper/2210.06583/) (2022)
- [Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays](https://mlsanity.com/paper/2301.12636/) (2023)
- [Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods](https://mlsanity.com/paper/2305.19079/) (2023)
- [Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement](https://mlsanity.com/paper/2501.13514/) (2025)
- [Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction](https://mlsanity.com/paper/2204.10436/) (2022)
- [PINNs for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2408.01026/) (2024)
- [Benchmarking Self-Supervised Learning Methods for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2502.14009/) (2025)
- [GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction](https://mlsanity.com/paper/2207.08393/) (2022)
- [Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classification](https://mlsanity.com/paper/2304.11529/) (2023)
- [Efficient Noise Calculation in Deep Learning-based MRI Reconstructions](https://mlsanity.com/paper/2505.02007/) (2025)
- [Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction](https://mlsanity.com/paper/2312.12644/) (2023)
- [Inference Stage Denoising for Undersampled MRI Reconstruction](https://mlsanity.com/paper/2402.08692/) (2024)
- [MedXplore: Towards Reliable and Unbiased Generalized Category Discovery in Medical Imaging](https://mlsanity.com/paper/2607.27620/) (2026)
- [Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior](https://mlsanity.com/paper/2609.01959/) (2026)

## Abstract (arXiv)

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.