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Title: Monte Carlo based investigation of Berry phase for depth resolved characterization of biomedical scattering samples

The propagation of light in turbid media is an active area of research with relevance to numerous investigational fields, e.g., biomedical diagnostics and therapeutics. The statistical random-walk nature of photon propagation through turbid media is ideal for computational based modeling and simulation. Ready access to super computing resources provide a means for attaining brute force solutions to stochastic light-matter interactions entailing scattering by facilitating timely propagation of sufficient (>10million) photons while tracking characteristic parameters based on the incorporated physics of the problem. One such model that works well for isotropic but fails for anisotropic scatter, which is the case for many biomedical sample scattering problems, is the diffusion approximation. In this report, we address this by utilizing Berry phase (BP) evolution as a means for capturing anisotropic scattering characteristics of samples in the preceding depth where the diffusion approximation fails. We extend the polarization sensitive Monte Carlo method of Ramella-Roman, et al.,1 to include the computationally intensive tracking of photon trajectory in addition to polarization state at every scattering event. To speed-up the computations, which entail the appropriate rotations of reference frames, the code was parallelized using OpenMP. The results presented reveal that BP is strongly correlated to the photonmore » penetration depth, thus potentiating the possibility of polarimetric depth resolved characterization of highly scattering samples, e.g., biological tissues.« less
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  1. ORNL
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Resource Relation:
Conference: Photonics West the SPIE International Symposium on Biomedical Optics, San Francisco, CA, USA, 20150207, 20150212
Research Org:
Oak Ridge National Laboratory (ORNL); Joint Institute for Computational Sciences (JICS)
Sponsoring Org:
ORNL LDRD Seed-Money
Country of Publication:
United States
Berry phase; geometric phase; polarization sensitive Monte Carlo; Monte Carlo modeling; biomedical samples; scattering samples; Polarimetry; back scattered Mueller matrix