Monte Carlo (MC) simulations are widely used in the field of medical biophysics, particularly for modelling light propagation in biological tissue. The iterative nature of MC simulations and their high computation time currently limit their use to solving the forward solution for a given set of source characteristics and tissue optical properties. However, applications such as photodynamic therapy treatment planning or image reconstruction in diffuse optical tomography require solving the inverse problem given a desired light dose distribution or absorber distribution,
respectively. A faster means for performing MC simulations would enable the use of MC-based models for such tasks. In this thesis, a gold standard MC code called MCML was accelerated using two distinct hardware-based approaches, namely designing custom hardware on field-programmable gate arrays (FPGAs) and programming commodity graphics processing units (GPUs). Currently, the GPU-based approach is promising, offering approximately 1000-fold speedup with 4 GPUs compared to an Intel Xeon CPU.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/18822 |
| Date | 15 February 2010 |
| Creators | Lo, William Chun Yip |
| Contributors | Lilge, Lothar, Rose, Jonathan S. |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0017 seconds