TY - JOUR
T1 - Denoising low-field MR images with a deep learning algorithm based on simulated data from easily accessible open-source software
AU - Salehi, Aram
AU - Mach, Mathieu
AU - Najac, Chloe
AU - Lena, Beatrice
AU - O'Reilly, Thomas
AU - Dong, Yiming
AU - Börnert, Peter
AU - Adams, Hieab
AU - Evans, Tavia
AU - Webb, Andrew
N1 - Publisher Copyright: © 2024 The Author(s)
PY - 2025/1
Y1 - 2025/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85211148378&partnerID=8YFLogxK
U2 - 10.1016/j.jmr.2024.107812
DO - 10.1016/j.jmr.2024.107812
M3 - Article
C2 - 39647413
AN - SCOPUS:85211148378
SN - 1090-7807
VL - 370
JO - Journal of Magnetic Resonance
JF - Journal of Magnetic Resonance
M1 - 107812
ER -