Training a neural network for Gibbs and noise removal in diffusion MRI.

ABSTRACT:

Purpose

To develop and evaluate a neural network-based method for Gibbs artifact and noise removal.

Methods

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.

Results

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.

Conclusions

The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine lea

SUBMITTER: Muckley MJ 

PROVIDER: S-EPMC7722184 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature