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

doi:10.1016/j.jmr.2024.107812

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

J Magn Reson. 2024 Nov 29:370:107812. doi: 10.1016/j.jmr.2024.107812. Online ahead of print.

Authors

Affiliations

¹ Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands; Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
² Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
³ Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands; Philips Innovative Technologies, Hamburg, Germany.
⁴ Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands; Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile.
⁵ Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
⁶ Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands. Electronic address: [email protected].

PMID: 39647413
DOI: 10.1016/j.jmr.2024.107812

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.

Keywords: Convolutional neural networks; Denoising; In vivo MRI; Low field MRI.