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A highly optimized Monte Carlo (MC) code package for simulating light transport is developed on the latest graphics processing unit (GPU) built for general-purpose computing from NVIDIA - the Fermi GPU. In biomedical optics, the MC method is the gold standard approach for simulating light transport in biological tissue, both due to its accuracy and its flexibility in modelling realistic, heterogeneous tissue geometry in 3-D. However, the widespread use of MC simulations in inverse problems, such as treatment planning for PDT, is limited by their long computation time. Despite its parallel nature, optimizing MC code on the GPU has been shown to be a challenge, particularly when the sharing of simulation result matrices among many parallel threads demands the frequent use of atomic instructions to access the slow GPU global memory. This paper proposes an optimization scheme that utilizes the fast shared memory to resolve the performance bottleneck caused by atomic access, and discusses numerous other optimization techniques needed to harness the full potential of the GPU. Using these techniques, a widely accepted MC code package in biophotonics, called MCML, was successfully accelerated on a Fermi GPU by approximately 600x compared to a state-of-the-art Intel Core i7 CPU. A skin model consisting of 7 layers was used as the standard simulation geometry. To demonstrate the possibility of GPU cluster computing, the same GPU code was executed on four GPUs, showing a linear improvement in performance with an increasing number of GPUs. The GPU-based MCML code package, named GPU-MCML, is compatible with a wide range of graphics cards and is released as an open-source software in two versions: an optimized version tuned for high performance and a simplified version for beginners ().

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Significant improvements in the accuracy of time-resolved diffuse reflectance spectroscopy are reached by using a Monte Carlo scheme for evaluation of measured photon time-of-flight distributions. The use of time-resolved diffusion theory of photon migration, being the current standard scheme for data evaluation, is shown defective. In particular, the familiar problem sometimes referred to as absorption-to-scattering coupling or crosstalk, is identified as an error related to the breakdown of the diffusion approximation. These systematic errors are investigated numerically using Monte Carlo simulations, and their influence on data evaluation of experimental recordings are accurately predicted. The proposed Monte Carlo-based data evaluation avoids these errors, and can be used for routine data evaluation. The accuracy and reproducibility of both MC and diffusion modeling are investigated experimentally using the MEDPHOT set of solid tissue-simulating phantoms, and provides convincing arguments that Monte Carlo-based evaluation is crucial in important ranges of optical properties. In contrast to diffusion-based evaluation, the Monte Carlo scheme results in optical properties consistent with phantom design. Since the MEDPHOT phantoms are used for international comparisons and performance assessment, the performed characterization is carefully reported.