Computational model of OCT in lung tissue

David C. Reed; Charles A. DiMarzio

doi:10.1117/12.842461

24 February 2010 Computational model of OCT in lung tissue

David C. Reed, Charles A. DiMarzio

Proceedings Volume 7570, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVII; 75700I (2010) https://doi.org/10.1117/12.842461
Event: SPIE BiOS, 2010, San Francisco, California, United States

Abstract

Lung research may have significant impact on human health. As two examples, recovery from collapse of the alveoli and the severe post surgery declines in forced vital capacity in patients under the effects of anesthesia are both poorly understood. Optical imaging is important to lung research for its inherently high resolution. Microscopy and color imaging are fundamentals of medicine, but interior lung tissue is usually viewed either endoscopically or ex vivo, stained slices. Techniques such as confocal microscopy and optical coherence tomography (OCT) have become increasingly popular in medical imaging because of their sectioning and depth penetration. Since OCT has the ability to achieve higher depth penetration than confocal it is more widely used in lung imaging, despite the difficulty of interpreting the images due to the poor numerical aperture (NA). To understand light propagation through the highly reflective and refractive surfaces of the lung, we developed a Finite-Difference Time Domain (FDTD) simulation. FDTD solves a discrete approximation to Maxwell's equations. Initial simulations have shown that structure up to 30 - 40μm below the surface is clearly visible. Deeper structures are hard to interpret, because of light scattering, compounded by speckle associated with coherent detection. Further simulations and experimental imaging may lead to improved collection and processing of images at deeper levels.

Citation Download Citation

David C. Reed and Charles A. DiMarzio "Computational model of OCT in lung tissue", Proc. SPIE 7570, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVII, 75700I (24 February 2010); https://doi.org/10.1117/12.842461

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