Collaborative Research ITR/NGS: An Integrated Simulation Environment for High-Resolution Computational Methods in Electromagnetics with Biomedical Applications

William Marsh Rice University, Houston TX

Investigators

Abstract

Current technologies for radiationbased treatment of cancerous tumors rely almost exclusively on diagnostic images such as MRI and PET scans to enable a careful targeting of multiple high-intensity beams. However, the very complex nature of the penetration of radiation energy into the biological tissue makes such targeting difficult and error-prone, possibly even prohibiting radiative treatment due to the risk of damaging essential tissue in close proximity to the cancerous areas. Such issues are naturally of particular concern in relation to treatment of brain cancer. This project will conduct research on the development of a simulation environment which eventually will provide a virtual patient-specific model of the area of interest and an ability to accurately and efficiently model wave-propagation within such an environment, with the potential to ultimately provide the radiation specialist with an online tool for fine tuning the targeting of radiation energy and, at a future stage, perhaps even model the impact of the energy deposition and heat release on the tissue. The project will develop an environment comprising of (a) the cleaning and segmentation of MRI data, including data with noise sensitivity, (b) extraction of material data and construction of a patient-specific volume model of the target of interest, (c) the generation of high-order, curvilinear, finite elements grids, (d) full as well as reduced order modeling of the penetration/refraction of electromagnetic energy into the volume model, and (e) visualization and extraction of physiological data of interest. These different elements will be integrated into a flexible, stand-alone environment and will, as part of the development, be tested extensively on phantom data as well as real MRI data, possibly with added artificial noise to explore robustness. The key developments will include new image segmentation and cleaning algorithms, improved material models, the development of efficient high-order accurate computational schemes for wave-propagation, efficient methods for domain truncation, and tools for visualization and data extraction. These are all problems of generic importance with potential for impact well beyond the particular application being considered.