LOCALLY ADAPTIVE HALF-MAX METHODS FOR AIRWAY LUMEN-AREA AND WALL-THICKNESS AND THEIR REPEAT CT SCAN REPRODUCIBILITY

Quantitative computed tomography (CT)-based characterization of bronchial metrics is increasingly being used to investigate chronic obstructive pulmonary disease (COPD)-related phenotypes. Automated methods for airway measurements benefit large multi-site studies by reducing cost and subjectivity errors. Critical challenges for CT-based analysis of airway morphology are related to location of lumen and wall transitions in the presence of varying scales and intensity-contrasts from proximal to distal sites. This paper introduces locally adaptive half-max methods to locate airway lumen and wall transitions and compute cross-sectional lumen area and wall-thickness. Also, the method uses a consistency analysis of wall-thickness to avoid adjoining-structure-artifacts. Experimental results show that computed bronchial measures at individual anatomic airway tree locations are repeat CT scan reproducible with intra-class correlation coefficient (ICC) values exceeding 0.9 and 0.8 for lumen-area and wall-thickness, respectively. Observed ICC values for derived morphologic measures, e.g., lumen-area compactness (ICC>0.67) and tapering (ICC>0.47) are relatively lower.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:2020

Enthalten in:

Proceedings. IEEE International Symposium on Biomedical Imaging - 2020(2020) vom: 01. Apr.

Sprache:

Englisch

Beteiligte Personen:

Nadeem, Syed Ahmed [VerfasserIn]
Hoffman, Eric A [VerfasserIn]
Comellas, Alejandro P [VerfasserIn]
Saha, Punam K [VerfasserIn]

Links:

Volltext

Themen:

Airway measurements
Airway tree
COPD
Computed tomography
Journal Article
Wall thickness

Anmerkungen:

Date Revised 24.08.2021

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1109/isbi45749.2020.9098558

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM329655841