Denoising low-field MR images with a deep learning algorithm based on simulated data from easily accessible open-source software

Aram Salehi, Mathieu Mach, Chloe Najac, Beatrice Lena, Thomas O'Reilly, Yiming Dong, Peter Börnert, Hieab Adams, Tavia Evans, Andrew Webb*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In this study, we introduce a denoising method aimed at improving the contrast ratio in low-field MRI (LFMRI) using an advanced 3D deep convolutional residual network model. Our approach employs synthetic brain imaging datasets that closely mimic the contrast and noise characteristics of LFMRI scans, addressing the limitation of available in-vivo LFMRI datasets for training deep learning models. In the simulation data, the Relative Contrast Ratio (RCR) increased, and similar improvements were observed in the in-vivo data across different imaging conditions. Comparative evaluations demonstrate that our model performs better than the widely used non-deep learning method, BM4D, in enhancing RCR and maintaining high spatial frequency components in in-vivo data.

Original languageEnglish
Article number107812
JournalJournal of Magnetic Resonance
Volume370
Early online date29 Nov 2024
DOIs
Publication statusPublished - Jan 2025

Bibliographical note

Publisher Copyright: © 2024 The Author(s)

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