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Proceedings Paper

Evaluation of a reflectance-based approach for optical property determination in layered tissue
Author(s): Quanzeng Wang; Anant Agrawal; Nam Sun Wang; Josh Pfefer
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Paper Abstract

In order to elucidate light propagation mechanisms involved in optical spectroscopy devices, the optical properties of layered mucosal tissues at ultraviolet and visible wavelengths are needed. Previous approaches to measuring this data have typically been based on spatially-resolved reflectance. However, these approaches have limitations, some of which are not well understood. Therefore, the objectives of this study were (1) to elucidate the relationship between spatially-resolved reflectance distributions and optical properties in two-layer tissue models and (2) introduce and assess an unconstrained approach to optical property measurement. The first part of this study involved calculating reflectance from two-layer tissue for a wide variety of optical property combinations (πa = 1-22.5, πs' = 5-42.5 cm-1) using a Monte Carlo scaling technique. In the second part, a neural network inverse model trained with the aforementioned results was evaluated using simulated reflectance data. This relationship between optical properties and reflectance provides fundamental insights into the strengths, weaknesses and potential limitations of strategies for optical property measurement based on spatially-resolved reflectance. The neural network approach estimated optical property values with a degree of accuracy that depended on the probe geometry (5-, 6-, 10- and 11-fiber probes were simulated). The average error in determination of πa ranged from 15 to 51% and average error for πs' ranged from 8 to 32%. While computationally expensive to develop, neural network models calibrated with simulation data may prove to be a highly effective approach for rapid, unconstrained estimation of the optical properties of two-layer tissues.

Paper Details

Date Published: 25 February 2009
PDF: 11 pages
Proc. SPIE 7170, Design and Quality for Biomedical Technologies II, 71700I (25 February 2009); doi: 10.1117/12.813847
Show Author Affiliations
Quanzeng Wang, U.S. Food and Drug Administration (United States)
Univ. of Maryland, College Park (United States)
Anant Agrawal, U.S. Food and Drug Administration (United States)
Nam Sun Wang, Univ. of Maryland, College Park (United States)
Josh Pfefer, U.S. Food and Drug Administration (United States)


Published in SPIE Proceedings Vol. 7170:
Design and Quality for Biomedical Technologies II
Ramesh Raghavachari; Rongguang Liang, Editor(s)

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