Title | Training a neural network for Gibbs and noise removal in diffusion MRI. |
Publication Type | Journal Article |
Year of Publication | 2021 |
Authors | Muckley MJ, Ades-Aron B, Papaioannou A, Lemberskiy G, Solomon E, Lui YW, Sodickson DK, Fieremans E, Novikov DS, Knoll F |
Journal | Magn Reson Med |
Volume | 85 |
Issue | 1 |
Pagination | 413-428 |
Date Published | 2021 01 |
ISSN | 1522-2594 |
Keywords | Artifacts, Diffusion Magnetic Resonance Imaging, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neural Networks, Computer |
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. |
DOI | 10.1002/mrm.28395 |
Alternate Journal | Magn Reson Med |
PubMed ID | 32662910 |
PubMed Central ID | PMC7722184 |
Grant List | P30 AG066512 / AG / NIA NIH HHS / United States P41 EB017183 / EB / NIBIB NIH HHS / United States R01 NS088040 / NS / NINDS NIH HHS / United States R01 EB024532 / EB / NIBIB NIH HHS / United States |
Related Institute:
MRI Research Institute (MRIRI)