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Automated analysis method to assess pulmonary blood flow distribution using conventional X-ray angiography
Quantitative assessment of the right-to-left ratio of pulmonary blood flow distribution is important for determining the clinical indications for treating pulmonary arterial branch stenosis. A novel theory was recently proposed that can be used to quantitatively assess the right-to-left ratio on con...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395340/ https://www.ncbi.nlm.nih.gov/pubmed/35995924 http://dx.doi.org/10.1038/s41598-022-18627-5 |
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author | Sakaguchi, Takuya Watanabe, Yuichiro Hirose, Masashi Takei, Kohta Yasukochi, Satoshi |
author_facet | Sakaguchi, Takuya Watanabe, Yuichiro Hirose, Masashi Takei, Kohta Yasukochi, Satoshi |
author_sort | Sakaguchi, Takuya |
collection | PubMed |
description | Quantitative assessment of the right-to-left ratio of pulmonary blood flow distribution is important for determining the clinical indications for treating pulmonary arterial branch stenosis. A novel theory was recently proposed that can be used to quantitatively assess the right-to-left ratio on conventional X-ray angiography images. In the proposal, further developments were indicated, especially automated calculation. In this study, a new automated algorithm was developed. In the X-ray image, regions of interest were set in right and left lung, and time-signal intensity curves were measured. The new automated algorithm is applied to determine the optimal time window for the analysis of the time-signal intensity curve and to calculate the slope of the curve in the optimized time window. The right-to-left ratios in seven consecutive patients calculated by the new automated algorithm were compared to those calculated by lung perfusion scintigraphy. The ratios were in good agreement with linear regression with a slope of 1.27 and a Pearson correlation coefficient of 0.95. The processing time was less than 10 s, which is one-eighth of the manual processing time. The new automated algorithm is accurate, stable, and fast enough for clinical use in the real world. |
format | Online Article Text |
id | pubmed-9395340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93953402022-08-24 Automated analysis method to assess pulmonary blood flow distribution using conventional X-ray angiography Sakaguchi, Takuya Watanabe, Yuichiro Hirose, Masashi Takei, Kohta Yasukochi, Satoshi Sci Rep Article Quantitative assessment of the right-to-left ratio of pulmonary blood flow distribution is important for determining the clinical indications for treating pulmonary arterial branch stenosis. A novel theory was recently proposed that can be used to quantitatively assess the right-to-left ratio on conventional X-ray angiography images. In the proposal, further developments were indicated, especially automated calculation. In this study, a new automated algorithm was developed. In the X-ray image, regions of interest were set in right and left lung, and time-signal intensity curves were measured. The new automated algorithm is applied to determine the optimal time window for the analysis of the time-signal intensity curve and to calculate the slope of the curve in the optimized time window. The right-to-left ratios in seven consecutive patients calculated by the new automated algorithm were compared to those calculated by lung perfusion scintigraphy. The ratios were in good agreement with linear regression with a slope of 1.27 and a Pearson correlation coefficient of 0.95. The processing time was less than 10 s, which is one-eighth of the manual processing time. The new automated algorithm is accurate, stable, and fast enough for clinical use in the real world. Nature Publishing Group UK 2022-08-22 /pmc/articles/PMC9395340/ /pubmed/35995924 http://dx.doi.org/10.1038/s41598-022-18627-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 | Article Sakaguchi, Takuya Watanabe, Yuichiro Hirose, Masashi Takei, Kohta Yasukochi, Satoshi Automated analysis method to assess pulmonary blood flow distribution using conventional X-ray angiography |
title | Automated analysis method to assess pulmonary blood flow distribution using conventional X-ray angiography |
title_full | Automated analysis method to assess pulmonary blood flow distribution using conventional X-ray angiography |
title_fullStr | Automated analysis method to assess pulmonary blood flow distribution using conventional X-ray angiography |
title_full_unstemmed | Automated analysis method to assess pulmonary blood flow distribution using conventional X-ray angiography |
title_short | Automated analysis method to assess pulmonary blood flow distribution using conventional X-ray angiography |
title_sort | automated analysis method to assess pulmonary blood flow distribution using conventional x-ray angiography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395340/ https://www.ncbi.nlm.nih.gov/pubmed/35995924 http://dx.doi.org/10.1038/s41598-022-18627-5 |
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