Abstract
In the field of biomedical optics, use of light to detect cancerous tissue transformations often involves a low probability detector response because tissue is very turbid and scattering is highly forward-peaked. In these applications, we use a contributon map to extend the geometric learning of adaptive Monte Carlo algorithms. The contributon function provides a phase space map of the lossless flow of “contributon particles” that necessarily are transported from source to detector. This map is utilized within an adaptive sequential correlated sampling algorithm to lower the variance systematically and provide improved convergence rates over conventional Monte Carlo.
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Acknowledgements
We acknowledge support from the Laser Microbeam and Medical Program (LAMMP) a NIH Biomedical Technology Resource Center (P41-RR01192) and from the National Institutes of Health (NIH, K25-EB007309).
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Hayakawa, C.K., Kong, R., Spanier, J. (2012). Spatial/Angular Contributon Maps for Improved Adaptive Monte Carlo Algorithms. In: Plaskota, L., Woźniakowski, H. (eds) Monte Carlo and Quasi-Monte Carlo Methods 2010. Springer Proceedings in Mathematics & Statistics, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27440-4_23
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DOI: https://doi.org/10.1007/978-3-642-27440-4_23
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