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Quantitative Analysis for Lung Disease on Thin-Section CT

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DIAGNOSTICS
卷 13, 期 18, 页码 -

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MDPI
DOI: 10.3390/diagnostics13182988

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computed tomography; image reconstruction; artificial intelligence; densitometry; lung diseases; interstitial; pulmonary disease; chronic obstructive

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Thin-section CT is widely used for assessing respiratory function and morphology, providing precise measurements of lung structures. These quantitative image analysis methods have important applications in studying the early stages and progression of lung diseases, particularly valuable during the COVID-19 pandemic.
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 x 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses.

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