High-performance computing with graphics hardware for medical physics applications

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A vast array of numerical methods are used to solve problems in Medical Physics, ranging from purely analytical approaches to Monte Carlo techniques. These numerical methods are essential for instance in dose calculation in radiation oncology and in all image processing tasks, including tomographic image reconstruction in radiology and nuclear medicine. The accuracy of numerical solutions typically grows with the complexity of the model used to describe the physics of the problem. Monte Carlo techniques for example provide the most accurate solutions to dose calculation problems because they are based on fundamental physics processes. However, the computational burden and long execution times associated with physics-rich models are typically not compatible with clinical applications, which often require near real-time execution. In order to deploy physics-rich, more accurate algorithms in clinical environments, my research group has developed high-performance computing approaches based on Graphics Processing Units (GPUs). We have developed a recognized expertise in adapting Medical Physics problems to these massively parallel devices, using innovative strategies to significantly accelerate advanced dose calculation and tomographic reconstruction algorithms. Building upon these previous achievements made mostly in Radiation Oncology, we plan to continue this research program to further develop innovative applications in Radiology and Nuclear Medicine. The overall objective is to integrate advanced physics models into Medical Physics applications in order to reach better, more accurate solutions in acceptable timeframes.