# MRI Banding Removal via Adversarial Training

arXiv: https://arxiv.org/abs/2001.08699 · HTML: https://mlsanity.com/paper/2001.08699/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Zero-Shot Self-Supervised Learning for MRI Reconstruction](https://mlsanity.com/paper/2102.07737/) (2021)
- [A review and experimental evaluation of deep learning methods for MRI reconstruction](https://mlsanity.com/paper/2109.08618/) (2021)
- [Perceive and Calibrate: Analyzing and Enhancing Robustness of Medical Multi-Modal Large Language Models](https://mlsanity.com/paper/2512.21964/) (2025)
- [DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models](https://mlsanity.com/paper/2312.04853/) (2023)

## Abstract (arXiv)

MRI images reconstructed from sub-sampled Cartesian data using deep learning techniques often show a characteristic banding (sometimes described as streaking), which is particularly strong in low signal-to-noise regions of the reconstructed image. In this work, we propose the use of an adversarial loss that penalizes banding structures without requiring any human annotation. Our technique greatly reduces the appearance of banding, without requiring any additional computation or post-processing at reconstruction time. We report the results of a blind comparison against a strong baseline by a group of expert evaluators (board-certified radiologists), where our approach is ranked superior at banding removal with no statistically significant loss of detail.