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

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.

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Publication Details

Journal
Journal of Magnetic Resonance
Published
2024-11-29
DOI
https://doi.org/10.1016/j.jmr.2024.107812
Citations
10
Primary Topic
Image and Signal Denoising Methods
Type
article
Field-Weighted Citation Impact
2.49

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

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

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article en
10 citations

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.

Journal of Magnetic ResonanceVol. 370
Radboud University Nijmegen (NL), Universität Hamburg (DE), Adolfo Ibáñez University (CL), Leiden University Medical Center (NL), Erasmus MC (NL), Radboud University Medical Center (NL), University Medical Center Hamburg-Eppendorf (DE)
European Research Council, H2020 European Research Council
Openalex Percentile: Top 9%
Image and Signal Denoising Methods
2.49
DOIOpen Access PDFOpenAlex
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