High-Performance Computing in Medical Physics

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Context Several numerical methods are used in Medical Physics, ranging from purely analytical approaches to Monte Carlo techniques, and dedicated to tasks such as dose calculations, tomographic reconstruction and image processing. In general, the accuracy and the quality of solutions obtained with these methods increases with the realism (and complexity) of the model used to describe the physics of the problem. However, the long execution times associated with physics-rich models are usually not compatible with clinical workflows. In order to deploy physics-rich, more accurate algorithms in clinical environments, my research group has developed High-Performance Computing (HPC) approaches based on massively parallel Graphics Processing Units (GPUs) for several computing tasks in Radiology, Radiation Oncology, and Nuclear Medicine. For the 2018-2023 Discovery cycle, we will innovate by integrating data-driven approaches in Medical Physics applications that can benefit from the latest hardware and software developements in data sciences. Objective The overall objective of the proposed research program is to integrate advanced physics models as well as data-driven knowledge and techniques into Medical Physics applications in order to obtain better, more accurate solutions (images, dose maps) in clinically acceptable timeframes. Scientific approach We will continue the development of advanced, GPU-based tomographic reconstruction algorithms in CT and CBCT, with the objective of improving images while reducing the patient dose through a better handling of sparse and noisy data, notably through scatter corrections made possible by our fast GPU-based Monte Carlo code. Promising approaches using Convolutional Neural Networks (CNNs) will be used for noise regularization, in complement to the Total Variation approach we have used so far. We also plan to use the latest generation of machine learning-based image segmentation algorithms to automatically generate the contours of anatomical structures in CT data sets. The training of this algorithm, implemented with the TensorFlow framework, will proceed with existing high-quality and large data sets of images contoured by specialists at our institution. We also plan to combine these contours with our GPU-based Monte Carlo dose calculation engine to compute doses to organs in CT, which is the fundamental requirement for large scale population studies investigating the risk of radiation exposure from medical sources. The proposed research program also comprises activities at the interface of Nuclear Medicine and Radiation Oncology, aimed at developing a robust dosimetry and treatment planning platform for personalized radionuclide targeted therapy. Significance This research program will ultimately benefit patients and stakeholders in the healthcare sector, as well as industrial partners.