TECHNICAL NOTE
Year : 2022 | Volume
: 47 | Issue : 4 | Page : 387--393
Denoising using Noise2Void for low-field magnetic resonance imaging: A phantom study
Shinya Kojima, Toshimune Ito, Tatsuya Hayashi
Department of Radiological Technology, Faculty of Medical Technology, Teikyo University, Itabashi-ku, Tokyo, Japan
Correspondence Address:
Dr. Shinya Kojima
Department of Radiological Technology, Faculty of Medical Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605
Japan
To reduce noise for low-field magnetic resonance imaging (MRI) using Noise2Void (N2V) and to demonstrate the N2V validity. N2V is one of the denoising convolutional neural network methods that allows the training of a model without a noiseless clean image. In this study, a kiwi fruit was scanned using a 0.35 Tesla MRI system, and the image qualities at pre- and postdenoising were evaluated. Structural similarity (SSIM), signal-to-noise ratio (SNR), and contrast ratio (CR) were measured, and visual assessment of noise and sharpness was observed. Both SSIM and SNR were significantly improved using N2V (P < 0.05). CR was unchanged between pre- and postdenoising images. The results of visual assessment for noise revealed higher scores in postdenoising images than that in predenoising images. The sharpness scores of postdenoising images were high when SNR was low. N2V provides effective noise reduction and is a useful denoising technique in low-field MRI.
How to cite this article:
Kojima S, Ito T, Hayashi T. Denoising using Noise2Void for low-field magnetic resonance imaging: A phantom study.J Med Phys 2022;47:387-393
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How to cite this URL:
Kojima S, Ito T, Hayashi T. Denoising using Noise2Void for low-field magnetic resonance imaging: A phantom study. J Med Phys [serial online] 2022 [cited 2023 Mar 27 ];47:387-393
Available from: https://www.jmp.org.in/article.asp?issn=0971-6203;year=2022;volume=47;issue=4;spage=387;epage=393;aulast=Kojima;type=0 |
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