Parallel, multi-purpose Monte Carlo code for simulation of light propagation in segmented tissues

Abstract

We have developed a massive-parallel, multi-purpose Monte Carlo (MC) code for simulation of light propagation in complex structures modelled with e.g. magnetic resonance images. The code is designed to execute parallel threads on a Linux-based cluster of computers equipped with multiple graphical processing units (GPU) utilizing NVidia CUDA technology. We show steps one can take to implements such code itself. Furthermore, we provide methodology of building a MRI-based head model and populating it with realistic optical properties at excitation and fluorescence emission wavelengths during inflow of fluorescence agent (indocyanine green – ICG). The proposed code provides following original features: (i) Simulation of fluorescence light propagation in media with spatial distribution of multiple different fluorophores characterized by concentration, quantum yield and fluorescence lifetime. (ii) The fluorescence light tracking does not need extra photon tracking and works in parallel with tracking photons at the excitation wavelength, introducing execution time overhead of 0.02% only. (iii) Calculation of high-resolution spatial distributions of sensitivity factors mapping voxels absorption change to parameters measured on a model surface: statistical parameters (moments) of the distribution of time of flight of photons. (iv) Random number generators states are preserved between runs to greatly improve calculation time of the sensitivity factors. (v) Simulation of fluorescence lifetime wide field imaging in diffusively scattering media. The code is designed to handle big models, divided into e.g. 200 million voxels or more, which other methods struggle to handle. Simulations on a human head model with 0.3 mm voxel size require 2 GB of a GPU memory only. This is supported by developed non-standard floating-point data storage format. The proposed code is cross-validated with field-leading MC and finite element methods on the same hardware environment for varying model sizes and temporal resolutions. Tests revealed competitive execution time and high temporal resolution of boundary data for high spatial resolution of MRI-based head model.