# On the Robustness of deep learning-based MRI Reconstruction to image transformations

arXiv: https://arxiv.org/abs/2211.04930 · HTML: https://mlsanity.com/paper/2211.04930/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2022.

## Built on by (most cited first, 7 of 7)

- [Text-Visual Prompting for Efficient 2D Temporal Video Grounding](https://mlsanity.com/paper/2303.04995/) (2023)
- [Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification](https://mlsanity.com/paper/2309.05794/) (2023)
- [The Power of Few: Accelerating and Enhancing Data Reweighting with Coreset Selection](https://mlsanity.com/paper/2403.12166/) (2024)
- [Local monotone operator learning using non-monotone operators: MnM-MOL](https://mlsanity.com/paper/2312.00386/) (2023)
- [Robust MRI Reconstruction by Smoothed Unrolling (SMUG)](https://mlsanity.com/paper/2312.07784/) (2023)
- [VisRec: A Semi-Supervised Approach to Radio Interferometric Data Reconstruction](https://mlsanity.com/paper/2403.00897/) (2024)
- [Training-Free Adversarial Robustness in Computational MRI](https://mlsanity.com/paper/2501.01908/) (2025)

## Abstract (arXiv)

Although deep learning (DL) has received much attention in accelerated magnetic resonance imaging (MRI), recent studies show that tiny input perturbations may lead to instabilities of DL-based MRI reconstruction models. However, the approaches of robustifying these models are underdeveloped. Compared to image classification, it could be much more challenging to achieve a robust MRI image reconstruction network considering its regression-based learning objective, limited amount of training data, and lack of efficient robustness metrics. To circumvent the above limitations, our work revisits the problem of DL-based image reconstruction through the lens of robust machine learning. We find a new instability source of MRI image reconstruction, i.e., the lack of reconstruction robustness against spatial transformations of an input, e.g., rotation and cutout. Inspired by this new robustness metric, we develop a robustness-aware image reconstruction method that can defend against both pixel-wise adversarial perturbations as well as spatial transformations. Extensive experiments are also conducted to demonstrate the effectiveness of our proposed approaches.