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 learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.

journal_name

Magn Reson Med

authors

Muckley MJ,Ades-Aron B,Papaioannou A,Lemberskiy G,Solomon E,Lui YW,Sodickson DK,Fieremans E,Novikov DS,Knoll F

doi

10.1002/mrm.28395

subject

Has Abstract

pub_date

2021-01-01 00:00:00

pages

413-428

issue

1

eissn

0740-3194

issn

1522-2594

journal_volume

85

pub_type

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