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Computational analysis of six optical coherence tomography systems for vocal fold imaging: A comparison study

Published Web Location

https://doi.org/10.1002/lsm.23060
Abstract

Objectives

There have been many advancements in laryngeal imaging using optical coherence tomography (OCT), with varying system design and probes for use in research, office, and operating room settings. We evaluated the performance of six distinct OCT systems in imaging porcine vocal folds (cords) using computational image processing and segmentation.

Methods

Porcine vocal folds were scanned using six OCT systems. Imaging system and probe performance were quantitatively assessed for signal penetration, layer differentiation, and epithelium (EP) measurement. Fitted exponential decay curves with corresponding α constant and intensity thresholding segmentation were utilized to quantify the aforementioned parameters.

Results

The smallest average α constant and deepest signal penetration was of the SS-OCT 1700 nm 90 kHz microscope system (α = -1.74), followed by the SS-OCT 1310 nm 200 kHz VCSEL microscope system (α = -1.99), and SS-OCT 1310 nm 50 kHz rigid forward viewing endoscope system (α = -2.23). The EP was not readily visualized for three out of six systems, but was detected using automated segmentation. Average EP thickness (mean ± SD) was calculated as 55.79 ± 31.86 μm which agrees favorably with previous literature.

Conclusion

Comparisons of OCT systems are challenging, as they encompass different probe design, optical path, and lasers, depending on application. Practical evaluation of different systems using computer based quantitative image processing and segmentation revealed basic, constructive information, such as EP measurements. To further validate the comparisons of system performance with clinical usability, in vivo human laryngeal imaging will be conducted. Further development of automated image processing and segmentation can be useful in rapid analysis of information. Lasers Surg. Med. 51:412-422, 2019. © 2019 Wiley Periodicals, Inc.

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