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 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.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:13

Enthalten in:

Diagnostics (Basel, Switzerland) - 13(2023), 18 vom: 18. Sept.

Sprache:

Englisch

Beteiligte Personen:

Iwasawa, Tae [VerfasserIn]
Matsushita, Shoichiro [VerfasserIn]
Hirayama, Mariko [VerfasserIn]
Baba, Tomohisa [VerfasserIn]
Ogura, Takashi [VerfasserIn]

Links:

Volltext

Themen:

Artificial intelligence
Chronic obstructive
Computed tomography
Densitometry
Image reconstruction
Interstitial
Journal Article
Lung diseases
Pulmonary disease
Review

Anmerkungen:

Date Revised 03.10.2023

published: Electronic

Citation Status Publisher

doi:

10.3390/diagnostics13182988

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM362576254