title: Combined Monte Carlo and finite-difference time-domain modeling for biophotonic analysis: implications on reflectance-based diagnosis of epithelial precancer.
abstract: Monte Carlo (MC) modeling of photon transport in tissues is generally performed using simplified functions that only approximate the angular scattering properties of tissue constituents. However, such approximations may not be sufficient for fully characterizing tissue scatterers such as cells. Finite-difference time-domain (FDTD) modeling provides a flexible approach to compute realistic tissue phase functions that describe probability of scattering at different angles. We describe a computational framework that combines MC and FDTD modeling, and allows random sampling of scattering directions from FDTD phase functions. We carry out simulations to assess the influence of incorporating realistic FDTD phase functions on modeling spectroscopic reflectance signals obtained from normal and precancerous epithelial tissues. Simulations employ various fiber optic probe designs to analyze the sensitivity of different probe geometries to FDTD-generated phase functions. Combined MC/FDTD modeling results indicate that the form of the phase function used is an important factor in determining the reflectance profile of tissues, and detected reflectance intensity can change up to approximately 30% when a realistic FDTD phase function is used instead of an approximating function. The results presented need to be taken into account when developing photon propagation models or implementing inverse algorithms to extract optical properties from measurements.
authors.affiliation: University College London, Department of Medical Physics and Bioengineering, London WC1E 6BT, United Kingdom.
authors.forename: Cemre
authors.initials: C
authors.lastname: Kortun
authors.forename: Yasser R
authors.initials: YR
authors.lastname: Hijazi
authors.forename: Dizem
authors.initials: D
authors.lastname: Arifler
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D009375:Neoplasms, Glandular and Epithelial / Q000175:diagnosis*; D010783:Photometry / Q000379:methods*; D017785:Photons; D011230:Precancerous Conditions / Q000175:diagnosis*; D012680:Sensitivity and Specificity