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

## TL;DR

This paper presents a neural network approach for removing Gibbs artifacts and noise in diffusion MRI, improving image quality and artifact correction flexibility in clinical settings.

## Contribution

Introduces CNN models for artifact removal in diffusion MRI, including complex image processing, trained on synthetic data, enhancing artifact mitigation capabilities.

## Key findings

- CNNs effectively reduce artifacts in diffusion-weighted images
- Complex image CNN reduces artifacts in partial Fourier acquisitions
- Method is applicable slice-by-slice for clinical use

## Abstract

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.