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Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography

To minimize radiation risk, dose reduction is important in the diagnostic and therapeutic applications of computed tomography (CT). However, image noise degrades image quality owing to the reduced X-ray dose and a possible unacceptably reduced diagnostic performance. Deep learning approaches with co...

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Autores principales: Usui, Keisuke, Ogawa, Koichi, Goto, Masami, Sakano, Yasuaki, Kyougoku, Shinsuke, Daida, Hiroyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8310822/
https://www.ncbi.nlm.nih.gov/pubmed/34304321
http://dx.doi.org/10.1186/s42492-021-00087-9
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author Usui, Keisuke
Ogawa, Koichi
Goto, Masami
Sakano, Yasuaki
Kyougoku, Shinsuke
Daida, Hiroyuki
author_facet Usui, Keisuke
Ogawa, Koichi
Goto, Masami
Sakano, Yasuaki
Kyougoku, Shinsuke
Daida, Hiroyuki
author_sort Usui, Keisuke
collection PubMed
description To minimize radiation risk, dose reduction is important in the diagnostic and therapeutic applications of computed tomography (CT). However, image noise degrades image quality owing to the reduced X-ray dose and a possible unacceptably reduced diagnostic performance. Deep learning approaches with convolutional neural networks (CNNs) have been proposed for natural image denoising; however, these approaches might introduce image blurring or loss of original gradients. The aim of this study was to compare the dose-dependent properties of a CNN-based denoising method for low-dose CT with those of other noise-reduction methods on unique CT noise-simulation images. To simulate a low-dose CT image, a Poisson noise distribution was introduced to normal-dose images while convoluting the CT unit-specific modulation transfer function. An abdominal CT of 100 images obtained from a public database was adopted, and simulated dose-reduction images were created from the original dose at equal 10-step dose-reduction intervals with a final dose of 1/100. These images were denoised using the denoising network structure of CNN (DnCNN) as the general CNN model and for transfer learning. To evaluate the image quality, image similarities determined by the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were calculated for the denoised images. Significantly better denoising, in terms of SSIM and PSNR, was achieved by the DnCNN than by other image denoising methods, especially at the ultra-low-dose levels used to generate the 10% and 5% dose-equivalent images. Moreover, the developed CNN model can eliminate noise and maintain image sharpness at these dose levels and improve SSIM by approximately 10% from that of the original method. In contrast, under small dose-reduction conditions, this model also led to excessive smoothing of the images. In quantitative evaluations, the CNN denoising method improved the low-dose CT and prevented over-smoothing by tailoring the CNN model.
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spelling pubmed-83108222021-08-16 Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography Usui, Keisuke Ogawa, Koichi Goto, Masami Sakano, Yasuaki Kyougoku, Shinsuke Daida, Hiroyuki Vis Comput Ind Biomed Art Original Article To minimize radiation risk, dose reduction is important in the diagnostic and therapeutic applications of computed tomography (CT). However, image noise degrades image quality owing to the reduced X-ray dose and a possible unacceptably reduced diagnostic performance. Deep learning approaches with convolutional neural networks (CNNs) have been proposed for natural image denoising; however, these approaches might introduce image blurring or loss of original gradients. The aim of this study was to compare the dose-dependent properties of a CNN-based denoising method for low-dose CT with those of other noise-reduction methods on unique CT noise-simulation images. To simulate a low-dose CT image, a Poisson noise distribution was introduced to normal-dose images while convoluting the CT unit-specific modulation transfer function. An abdominal CT of 100 images obtained from a public database was adopted, and simulated dose-reduction images were created from the original dose at equal 10-step dose-reduction intervals with a final dose of 1/100. These images were denoised using the denoising network structure of CNN (DnCNN) as the general CNN model and for transfer learning. To evaluate the image quality, image similarities determined by the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were calculated for the denoised images. Significantly better denoising, in terms of SSIM and PSNR, was achieved by the DnCNN than by other image denoising methods, especially at the ultra-low-dose levels used to generate the 10% and 5% dose-equivalent images. Moreover, the developed CNN model can eliminate noise and maintain image sharpness at these dose levels and improve SSIM by approximately 10% from that of the original method. In contrast, under small dose-reduction conditions, this model also led to excessive smoothing of the images. In quantitative evaluations, the CNN denoising method improved the low-dose CT and prevented over-smoothing by tailoring the CNN model. Springer Singapore 2021-07-25 /pmc/articles/PMC8310822/ /pubmed/34304321 http://dx.doi.org/10.1186/s42492-021-00087-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Usui, Keisuke
Ogawa, Koichi
Goto, Masami
Sakano, Yasuaki
Kyougoku, Shinsuke
Daida, Hiroyuki
Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography
title Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography
title_full Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography
title_fullStr Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography
title_full_unstemmed Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography
title_short Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography
title_sort quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8310822/
https://www.ncbi.nlm.nih.gov/pubmed/34304321
http://dx.doi.org/10.1186/s42492-021-00087-9
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