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Quantitative Analysis for Lung Disease on Thin-Section CT
Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated wi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10528918/ https://www.ncbi.nlm.nih.gov/pubmed/37761355 http://dx.doi.org/10.3390/diagnostics13182988 |
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author | Iwasawa, Tae Matsushita, Shoichiro Hirayama, Mariko Baba, Tomohisa Ogura, Takashi |
author_facet | Iwasawa, Tae Matsushita, Shoichiro Hirayama, Mariko Baba, Tomohisa Ogura, Takashi |
author_sort | Iwasawa, Tae |
collection | PubMed |
description | Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses. |
format | Online Article Text |
id | pubmed-10528918 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105289182023-09-28 Quantitative Analysis for Lung Disease on Thin-Section CT Iwasawa, Tae Matsushita, Shoichiro Hirayama, Mariko Baba, Tomohisa Ogura, Takashi Diagnostics (Basel) Review Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses. MDPI 2023-09-18 /pmc/articles/PMC10528918/ /pubmed/37761355 http://dx.doi.org/10.3390/diagnostics13182988 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Iwasawa, Tae Matsushita, Shoichiro Hirayama, Mariko Baba, Tomohisa Ogura, Takashi Quantitative Analysis for Lung Disease on Thin-Section CT |
title | Quantitative Analysis for Lung Disease on Thin-Section CT |
title_full | Quantitative Analysis for Lung Disease on Thin-Section CT |
title_fullStr | Quantitative Analysis for Lung Disease on Thin-Section CT |
title_full_unstemmed | Quantitative Analysis for Lung Disease on Thin-Section CT |
title_short | Quantitative Analysis for Lung Disease on Thin-Section CT |
title_sort | quantitative analysis for lung disease on thin-section ct |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10528918/ https://www.ncbi.nlm.nih.gov/pubmed/37761355 http://dx.doi.org/10.3390/diagnostics13182988 |
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