# Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs

arXiv: https://arxiv.org/abs/1809.06276 · HTML: https://mlsanity.com/paper/1809.06276/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution](https://mlsanity.com/paper/2108.03920/) (2021)
- [InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration](https://mlsanity.com/paper/2211.05622/) (2022)
- [HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis](https://mlsanity.com/paper/2405.04902/) (2024)
- [A Deep Generative Model of Neonatal Cortical Surface Development](https://mlsanity.com/paper/2206.07542/) (2022)
- [Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNs](https://mlsanity.com/paper/2303.17239/) (2023)

## Abstract (arXiv)

Motion artifacts are a primary source of magnetic resonance (MR) image quality deterioration with strong repercussions on diagnostic performance. Currently, MR motion correction is carried out either prospectively, with the help of motion tracking systems, or retrospectively by mainly utilizing computationally expensive iterative algorithms. In this paper, we utilize a new adversarial framework, titled MedGAN, for the joint retrospective correction of rigid and non-rigid motion artifacts in different body regions and without the need for a reference image. MedGAN utilizes a unique combination of non-adversarial losses and a new generator architecture to capture the textures and fine-detailed structures of the desired artifact-free MR images. Quantitative and qualitative comparisons with other adversarial techniques have illustrated the proposed model performance.