The simulation of light propagation through tissues is important for medical applications, such as photodynamic therapy (PDT) for cancer treatment. To optimize PDT an inverse problem, which works backwards from a desired distribution of light to the parameters that caused it, must be solved. These problems have no closed-form solution and therefore must be solved numerically using an iterative method. This involves running many forward light propagation simulations which is time-consuming and computationally intensive.

Biophotonics, the study of light propagation through living tissue, is important for many medical applications ranging from imaging and detection through therapy for conditions such as cancer. Effective medical use of light depends on simulating its propagation through highly-scattering tissue. Monte Carlo simulation of photon migration has been adopted as the “gold standard” for its ability to capture complicated geometries and model all of the relevant problem physics. This accuracy and generality comes at a high computational cost, which limits the technique's utility. Greatly generalizing previous work, we present the first and only hardware-accelerated Monte Carlo biophotonic simulator that can accept complicated geometries described by tetrahedral meshes. Implemented on an Altera Stratix V FPGA, it achieves high performance (4x) and extremely high energy efficiency (67x) compared to a tightly-optimized multi-threaded CPU implementation, with demonstrated potential to expand the performance gains even further to 15-20x, which would enable important clinical and research applications.

Photodynamic therapy (PDT) is a method of treating cancer that combines light and light-sensitive drugs to selectively destroy cancerous tumours without harming the healthy tissue. The success of PDT depends on the accurate computation of light dose distribution. Monte Carlo (MC) simulations can provide an accurate solution for light dose distribution, but have high computation time that prevents them from being used in treatment planning. To alleviate this problem, a hardware design of an MC simulation based on the gold standard software in biophotonics was implemented on a large modern FPGA. This implementation achieved a 28-fold speedup and 716-fold lower power-delay product compared to the gold standard software executed on a 3 GHz Intel Xeon 5160 processor. The accuracy of the hardware was compared to the gold standard using a realistic skin model. An experiment using 100 million photon packets yielded a light dose distribution that diverged by less than 0.1 mm. We also describe our development methodology, which employs an intermediate hardware description in SystemC prior to Verilog coding that led to significant design effort efficiency.