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

Matthew J. Muckley, Benjamin Ades-Aron, Antonios Papaioannou, Gregory Lemberskiy, Eddy Solomon, Yvonne W. Lui, Daniel K. Sodickson, Els Fieremans, Dmitry S. Novikov, Florian Knoll

    Research output: Contribution to journalArticlepeer-review

    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.

    Original languageEnglish (US)
    JournalMagnetic resonance in medicine
    DOIs
    StateAccepted/In press - 2020

    Keywords

    • denoising
    • diffusion MRI
    • Gibbs ringing
    • neural network

    ASJC Scopus subject areas

    • Radiology Nuclear Medicine and imaging

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