Abstract
A general survey is provided on the capability of Monte Carlo (MC) modeling in tissue optics while paying special attention to the recent progress in the development of methods for speeding up MC simulations. The principles of MC modeling for the simulation of light transport in tissues, which includes the general procedure of tracking an individual photon packet, common light-tissue interactions that can be simulated, frequently used tissue models, common contact/noncontact illumination and detection setups, and the treatment of time-resolved and frequency-domain optical measurements, are briefly described to help interested readers achieve a quick start. Following that, a variety of methods for speeding up MC simulations, which includes scaling methods, perturbation methods, hybrid methods, variance reduction techniques, parallel computation, and special methods for fluorescence simulations, as well as their respective advantages and disadvantages are discussed. Then the applications of MC methods in tissue optics, laser Doppler flowmetry, photodynamic therapy, optical coherence tomography, and diffuse optical tomography are briefly surveyed. Finally, the potential directions for the future development of the MC method in tissue optics are discussed.
Highlights
Monte Carlo (MC) methods are a category of computational methods that involve the random sampling of a physical quantity.[1,2] The term “the Monte Carlo method” can be traced back to 1940s,1 in which it was proposed to investigate neutron transport through various materials
The MC method is able to solve radiative transport equation (RTE) with any desired accuracy,[5] assuming that the required computational load is affordable. This method is viewed as the gold standard method to model light transport in tissues, results from which are frequently used as reference to validate other less rigorous methods such as diffuse approximation to the RTE.[6,7]
In the forward problem, light distribution is simulated for given optical properties, whereas in the inverse problem, optical properties are estimated by fitting the light distribution simulated by the MC method to experimentally measured values
Summary
Monte Carlo (MC) methods are a category of computational methods that involve the random sampling of a physical quantity.[1,2] The term “the Monte Carlo method” can be traced back to 1940s,1 in which it was proposed to investigate neutron transport through various materials Such a problem cannot be solved by conventional and deterministic mathematical methods. The MC method is able to solve radiative transport equation (RTE) with any desired accuracy,[5] assuming that the required computational load is affordable For this reason, this method is viewed as the gold standard method to model light transport in tissues, results from which are frequently used as reference to validate other less rigorous methods such as diffuse approximation to the RTE.[6,7] Due to its flexibility and recent advances in speed, the MC method has been explored in tissue optics to solve both the forward and inverse problems. In the forward problem, light distribution is simulated for given optical properties, whereas in the inverse problem, optical properties are estimated by fitting the light distribution simulated by the MC method to experimentally measured values
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