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Proceedings Paper

Endoscopic hyperspectral imaging: light guide optimization for spectral light source
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Paper Abstract

Hyperspectral imaging (HSI) is a technology used in remote sensing, food processing and documentation recovery. Recently, this approach has been applied in the medical field to spectrally interrogate regions of interest within respective substrates. In spectral imaging, a two (spatial) dimensional image is collected, at many different (spectral) wavelengths, to sample spectral signatures from different regions and/or components within a sample. Here, we report on the use of hyperspectral imaging for endoscopic applications. Colorectal cancer is the 3rd leading cancer for incidences and deaths in the US. One factor of severity is the miss rate of precancerous/flat lesions (~65% accuracy). Integrating HSI into colonoscopy procedures could minimize misdiagnosis and unnecessary resections. We have previously reported a working prototype light source with 16 high-powered light emitting diodes (LEDs) capable of high speed cycling and imaging. In recent testing, we have found our current prototype is limited by transmission loss (~99%) through the multi-furcated solid light guide (lightpipe) and the desired framerate (20-30 fps) could not be achieved. Here, we report on a series of experimental and modeling studies to better optimize the lightpipe and the spectral endoscopy system as a whole. The lightpipe was experimentally evaluated using an integrating sphere and spectrometer (Ocean Optics). Modeling the lightpipe was performed using Monte Carlo optical ray tracing in TracePro (Lambda Research Corp.). Results of these optimization studies will aid in manufacturing a revised prototype with the newly designed light guide and increased sensitivity. Once the desired optical output (5-10 mW) is achieved then the HIS endoscope system will be able to be implemented without adding onto the procedure time.

Paper Details

Date Published: 13 February 2018
PDF: 9 pages
Proc. SPIE 10487, Multimodal Biomedical Imaging XIII, 104870H (13 February 2018); doi: 10.1117/12.2290615
Show Author Affiliations
Craig M. Browning, Univ. of South Alabama (United States)
Samuel Mayes, Univ. of South Alabama (United States)
Thomas C. Rich, Univ. of South Alabama (United States)
Silas J. Leavesley, Univ. of South Alabama (United States)


Published in SPIE Proceedings Vol. 10487:
Multimodal Biomedical Imaging XIII
Fred S. Azar; Xavier Intes, Editor(s)

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