Polarization-based optical techniques have become increasingly popular in the field of biomedical diagnosis. In the current report we exploit the directional awareness of circularly and/or elliptically polarized light backscattered from turbid tissue-like scattering media. We apply circularly and elliptically polarized laser light which illuminates the samples of interest, and a standard optical polarimeter is used to observe the polarization state of light backscattered a few millimeters away from the point of incidence. We demonstrate that the Stokes vector of backscattered light depicted on a Poincaré sphere can be used to assess a turbid tissue-like scattering medium. By tracking the Stokes vector of the detected light on the Poincaré sphere, we investigate the utility of this approach for characterization of cancerous and non-cancerous tissue samples in vitro. The obtained results are discussed in the framework of a phenomenological model and the results of a polarization tracking Monte Carlo model, developed in house. Schematic illustration of the experimental approach utilizing circularly and elliptically polarized light for probing turbid tissue-like scattering media.
Conceptual engineering design and optimization of laser-based imaging techniques and optical diagnostic systems used in the field of biomedical optics requires a clear understanding of the light-tissue interaction and peculiarities of localization of the detected optical radiation within the medium. The description of photon migration within the turbid tissue-like media is based on the concept of radiative transfer that forms a basis of Monte Carlo (MC) modeling. An opportunity of direct simulation of influence of structural variations of biological tissues on the probing light makes MC a primary tool for biomedical optics and optical engineering. Due to the diversity of optical modalities utilizing different properties of light and mechanisms of light-tissue interactions a new MC code is typically required to be developed for the particular diagnostic application. In current paper introducing an object oriented concept of MC modeling and utilizing modern web applications we present the generalized online computational tool suitable for the major applications in biophotonics. The computation is supported by NVIDEA CUDA Graphics Processing Unit providing acceleration of modeling up to 340 times.
We developed a compact, hand-held hyperspectral imaging system for 2D neural network-based visualization of skin chromophores and blood oxygenation. State-of-the-art microoptic multichannel matrix sensor combined with the tunable Fabry-Perot micro interferometer enables a portable diagnostic device sensitive to the changes of the oxygen saturation as well as the variations of blood volume fraction of human skin. Generalized object-oriented Monte Carlo model is used extensively for the training of an artificial neural network utilized for the hyperspectral image processing. In addition, the results are verified and validated via actual experiments with tissue phantoms and human skin in vivo. The proposed approach enables a tool combining both the speed of an artificial neural network processing and the accuracy and flexibility of advanced Monte Carlo modeling. Finally, the results of the feasibility studies and the experimental tests on biotissue phantoms and healthy volunteers are presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.