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A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation

Computed tomography (CT) is a widely used medical imaging modality for diagnosing various diseases. Among CT techniques, 4-dimensional CT perfusion (4D-CTP) of the brain is established in most centers for diagnosing strokes and is considered the gold standard for hyperacute stroke diagnosis. However...

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Autores principales: Yang, WonSeok, Hong, Jun-Yong, Kim, Jeong-Youn, Paik, Seung-ho, Lee, Seung Hyun, Park, Ji-Su, Lee, Gihyoun, Kim, Beop Min, Jung, Young-Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309118/
https://www.ncbi.nlm.nih.gov/pubmed/32481740
http://dx.doi.org/10.3390/s20113063
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author Yang, WonSeok
Hong, Jun-Yong
Kim, Jeong-Youn
Paik, Seung-ho
Lee, Seung Hyun
Park, Ji-Su
Lee, Gihyoun
Kim, Beop Min
Jung, Young-Jin
author_facet Yang, WonSeok
Hong, Jun-Yong
Kim, Jeong-Youn
Paik, Seung-ho
Lee, Seung Hyun
Park, Ji-Su
Lee, Gihyoun
Kim, Beop Min
Jung, Young-Jin
author_sort Yang, WonSeok
collection PubMed
description Computed tomography (CT) is a widely used medical imaging modality for diagnosing various diseases. Among CT techniques, 4-dimensional CT perfusion (4D-CTP) of the brain is established in most centers for diagnosing strokes and is considered the gold standard for hyperacute stroke diagnosis. However, because the detrimental effects of high radiation doses from 4D-CTP may cause serious health risks in stroke survivors, our research team aimed to introduce a novel image-processing technique. Our singular value decomposition (SVD)-based image-processing technique can improve image quality, first, by separating several image components using SVD and, second, by reconstructing signal component images to remove noise, thereby improving image quality. For the demonstration in this study, 20 4D-CTP dynamic images of suspected acute stroke patients were collected. Both the images that were and were not processed via the proposed method were compared. Each acquired image was objectively evaluated using contrast-to-noise and signal-to-noise ratios. The scores of the parameters assessed for the qualitative evaluation of image quality improved to an excellent rating (p < 0.05). Therefore, our SVD-based image-denoising technique improved the diagnostic value of images by improving their quality. The denoising technique and statistical evaluation can be utilized in various clinical applications to provide advanced medical services.
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spelling pubmed-73091182020-06-25 A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation Yang, WonSeok Hong, Jun-Yong Kim, Jeong-Youn Paik, Seung-ho Lee, Seung Hyun Park, Ji-Su Lee, Gihyoun Kim, Beop Min Jung, Young-Jin Sensors (Basel) Article Computed tomography (CT) is a widely used medical imaging modality for diagnosing various diseases. Among CT techniques, 4-dimensional CT perfusion (4D-CTP) of the brain is established in most centers for diagnosing strokes and is considered the gold standard for hyperacute stroke diagnosis. However, because the detrimental effects of high radiation doses from 4D-CTP may cause serious health risks in stroke survivors, our research team aimed to introduce a novel image-processing technique. Our singular value decomposition (SVD)-based image-processing technique can improve image quality, first, by separating several image components using SVD and, second, by reconstructing signal component images to remove noise, thereby improving image quality. For the demonstration in this study, 20 4D-CTP dynamic images of suspected acute stroke patients were collected. Both the images that were and were not processed via the proposed method were compared. Each acquired image was objectively evaluated using contrast-to-noise and signal-to-noise ratios. The scores of the parameters assessed for the qualitative evaluation of image quality improved to an excellent rating (p < 0.05). Therefore, our SVD-based image-denoising technique improved the diagnostic value of images by improving their quality. The denoising technique and statistical evaluation can be utilized in various clinical applications to provide advanced medical services. MDPI 2020-05-28 /pmc/articles/PMC7309118/ /pubmed/32481740 http://dx.doi.org/10.3390/s20113063 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, WonSeok
Hong, Jun-Yong
Kim, Jeong-Youn
Paik, Seung-ho
Lee, Seung Hyun
Park, Ji-Su
Lee, Gihyoun
Kim, Beop Min
Jung, Young-Jin
A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation
title A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation
title_full A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation
title_fullStr A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation
title_full_unstemmed A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation
title_short A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation
title_sort novel singular value decomposition-based denoising method in 4-dimensional computed tomography of the brain in stroke patients with statistical evaluation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309118/
https://www.ncbi.nlm.nih.gov/pubmed/32481740
http://dx.doi.org/10.3390/s20113063
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