# Swap-Free Fat-Water Separation in Dixon MRI using Conditional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2107.14175 · HTML: https://mlsanity.com/paper/2107.14175/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018)
- [Distribution Matching Losses Can Hallucinate Features in Medical Image Translation](https://mlsanity.com/paper/1805.08841/) (2018)
- [Pixel Recursive Super Resolution](https://mlsanity.com/paper/1702.00783/) (2017)
- [Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction](https://mlsanity.com/paper/1806.11216/) (2018)
- [Large-scale biometry with interpretable neural network regression on UK Biobank body MRI](https://mlsanity.com/paper/2002.06862/) (2020)
- [Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants](https://mlsanity.com/paper/2006.06996/) (2020)
- [Large-Scale Analysis of Iliopsoas Muscle Volumes in the UK Biobank](https://mlsanity.com/paper/2008.05217/) (2020)
- [Separation of water and fat signal in whole-body gradient echo scans using convolutional neural networks](https://mlsanity.com/paper/1812.04922/) (2018)
- [Image Processing and Quality Control for Abdominal Magnetic Resonance Imaging in the UK Biobank](https://mlsanity.com/paper/2007.01251/) (2020)

## Built on by (most cited first, 1 of 1)

- [Synthesizing Proton-Density Fat Fraction and R2^* from 2-point Dixon MRI with Generative Machine Learning](https://mlsanity.com/paper/2410.11186/) (2024)

## Abstract (arXiv)

Dixon MRI is widely used for body composition studies. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed on the scanner, making the data difficult to analyse. The most common artifact are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which can be wasteful and lead to unintended biases. The UK Biobank is acquiring Dixon MRI for over 100,000 participants, and thousands of swaps will occur. If those go undetected, errors will propagate into processes such as abdominal organ segmentation and dilute the results in population-based analyses. There is a clear need for a fast and robust method to accurately separate fat and water channels. In this work we propose such a method based on style transfer using a conditional generative adversarial network. We also introduce a new Dixon loss function for the generator model. Using data from the UK Biobank Dixon MRI, our model is able to predict highly accurate fat and water channels that are free from artifacts. We show that the model separates fat and water channels using either single input (in-phase) or dual input (in-phase and opposed-phase), with the latter producing improved results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps.