# Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI

arXiv: https://arxiv.org/abs/1905.04176 · HTML: https://mlsanity.com/paper/1905.04176/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2019.

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

- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [Robust partial Fourier reconstruction for diffusion-weighted imaging using a recurrent convolutional neural network](https://mlsanity.com/paper/2105.09378/) (2021)
- [An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal](https://mlsanity.com/paper/2302.02849/) (2023)
- [Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning](https://mlsanity.com/paper/2206.00305/) (2022)
- [Generative AI for Rapid Diffusion MRI with Improved Image Quality, Reliability and Generalizability](https://mlsanity.com/paper/2303.05686/) (2023)
- [DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models](https://mlsanity.com/paper/2312.04853/) (2023)
- [MBD: Multi b-value Denoising of Diffusion Magnetic Resonance Images](https://mlsanity.com/paper/2410.16898/) (2024)

## Abstract (arXiv)

We develop and evaluate a neural network-based method for Gibbs artifact and noise removal. A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.