Quantitative Analysis for Lung Disease on Thin-Section CT

被引:4
作者
Iwasawa, Tae [1 ]
Matsushita, Shoichiro [1 ]
Hirayama, Mariko [1 ]
Baba, Tomohisa [2 ]
Ogura, Takashi [2 ]
机构
[1] Kanagawa Cardiovasc & Resp Ctr, Dept Radiol, 6-16-1 Tomioka Higashi,Kanazawa Ku, Yokohama 2360051, Japan
[2] Kanagawa Cardiovasc & Resp Ctr, Dept Resp Med, 6-16-1 Tomioka Higashi,Kanazawa Ku, Yokohama 2360051, Japan
关键词
computed tomography; image reconstruction; artificial intelligence; densitometry; lung diseases; interstitial; pulmonary disease; chronic obstructive; IDIOPATHIC PULMONARY-FIBROSIS; SMALL-AIRWAY OBSTRUCTION; GENOME-WIDE ASSOCIATION; COMPUTED-TOMOGRAPHY; OBJECTIVE QUANTIFICATION; MACROSCOPIC MORPHOMETRY; AIRSPACE ENLARGEMENT; FLOW LIMITATION; EMPHYSEMA; SMOKERS;
D O I
10.3390/diagnostics13182988
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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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页数:24
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