Method for retrospective, respiratory-gated, anatomical optical coherence tomography for airway wall elastography
Year: 2026
Type: article
Abstract: Significance: Airway wall elastography (AWE) is promising for evaluating upper airway obstructive disorders and airway injuries. Technologies for AWE based on endoscopic optical coherence tomography (OCT) provide micron-scale resolution to capture airway wall deformations during tidal breathing. Combined with an intraluminal pressure probe, these technologies can provide quantitative AWE as part of a routine bronchoscopy exam. However, scan times must be of short duration to mitigate risk. Aim: Our objective is to reduce the scan time necessary to perform OCT elastography over a 50 mm length of the airway wall to less than 1 min. Approach: We introduce an innovative, 4D OCT imaging technique that scans in a sawtooth pattern to revisit each axial position of the airway over a diversity of respiratory phases. An anatomical (long-range) OCT system capable of capturing cross-sections of the upper airway was employed in conjunction with an intraluminal pressure catheter. Scanned data are retrospectively sorted into axial bins with high- and low-pressure thresholds used to compute cross-sectional compliance (CC) within each bin across the length of the upper airway. Results: 4D OCT was tested in simulation, on rigid and deformable samples, and on in vivo pigs undergoing bronchoscopy. A precise CC measurement with a 0.5 mm sampling resolution over a 50 mm scan length in under 42 s was achieved. Conclusions: The retrospective, respiratory-gated 4D aOCT scanning method is a minimally invasive technique for measuring airway wall CC. The method exhibited high precision in controlled models, effectively detected elastic heterogeneity, and yielded clinically relevant results in in vivo pigs.
Source: UNC Libraries
DOI: 10.17615/axeg-f725
Institution: University of North Carolina at Chapel Hill
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7212399600
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