20.500.12556/RUL-128814 Fast and accurate Monte Carlo simulations of subdiffusive spatially resolved reflectance for a realistic optical fiber probe tip model aided by a deep neural network In this work, we introduce a framework for efficient and accurate Monte Carlo (MC) simulations of spatially resolved reflectance (SRR) acquired by optical fiber probes that account for all the details of the probe tip including reflectivity of the stainless steel and the properties of the epoxy fill and optical fibers. While using full details of the probe tip is essential for accurate MC simulations of SRR, the break-down of the radial symmetry in the detection scheme leads to about two orders of magnitude longer simulation times. The introduced framework mitigates this performance degradation, by an efficient reflectance regression model that maps SRR obtained by fast MC simulations based on a simplified probe tip model to SRR simulated using the full details of the probe tip. We show that a small number of SRR samples is sufficient to determine the parameters of the regression model. Finally, we use the regression model to simulate SRR for a stainless steel optical probe with six linearly placed fibers and experimentally validate the framework through the use of inverse models for estimation of absorption and reduced scattering coefficients and subdiffusive scattering phase function quantifiers. light propagation model Monte Carlo simulations absorption subdiffusive spatially resolved reflectance optical fiber probe deep neural networks deep learning model širjenja svetlobe simulacije Monte Carlo subdifuzijska prostorsko razločena reflektanca absorpcija optična sonda nevronske mreže globoko učenje true false true Angleški jezik Slovenski jezik V članku navedeno: "© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement"; s povezavo https://www.osapublishing.org/library/license_v1.cfm. (3. 8. 2021) Članek v reviji 2021-08-03 10:25:40 2021-08-03 10:25:43 2023-05-30 03:27:11 0000-00-00 00:00:00 2020 0 0 Str. 3875-3889 no. 7, art. 391163 Vol. 11 1 Jul. 2020 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 535:004.8 2156-7085 10.1364/BOE.391163 26951939 RAZ_Zelinskyi_Yevhen_2020.pdf RAZ_Zelinskyi_Yevhen_2020.pdf 1 B8EBE8278120F30C1DBC5732862463F6 1ebee17f5a98f79ac0ca39cc4b31376313424c6deee248dcc493d06002aeb0ae c12b940d-f434-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=145700 https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-11-7-3875&id=432821 0 39dd55b0-f434-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=145699 Fakulteta za elektrotehniko 0 0 0