The goal of this project is to develop a dynamic data-driven planning and control system for laser treatment of cancer. The proposed research includes
development of a general mathematical framework and a family of mathematical and computational models of bio-heat transfer, tissue damage, and tumor viability,
dynamic calibration, verification and validation processes based on laboratory and clinical data and simulated response, and
design of effective thermo-therapeutic protocols using model predictions.
At the core of the proposed systems is the adaptive-feedback control of mathematical and computational models based on a posteriori estimates of errors in key quantities of interest, and modern Magnetic Resonance Temperature Imaging (MRTI) and diode laser devices to monitor treatment of tumors in laboratory animals. This approach enables an automated systematic model selection process based on acceptance criteria determined a priori and is valid for models of events occurring at multiple spatial and temporal scales. The proposed project should be of interest to both NIH/NLM and NSF. The methodologies to be implemented involve uncertainty quantification methods designed to provide an innovative, data-driven, patient-specific approach to effective cancer treatment. The general mathematical framework resulting from this research will be applicable to any thermo-therapeutic cancer treatment, but our treatment protocols will be established based on tumors seeded in prostates of canines. The primary objective of the proposed research is to develop treatment strategies by selecting optimal parameter sets (such as laser power, wave length, and fluence rate) based on high fidelity model predictions and data from cellular and in vivo biological measurement, and MRTI thermal distributions. From the computational and computer sciences perspective, this research will lead to the development of state-of-the-art simulation tools that interact with a variety of dynamic measuring, visualization, and control systems over computational grids. New approaches to data-driven simulation will be developed based on the notion of adaptive modeling. From the biomedical perspective, the research should lead to the ability to induce predictable thermo-patterns in vivo through laser irradiation heating of cancerous tumors, and to investigate MRTI measured heating information, tissue response and damage to enable complete tumor destruction and the minimum damage of normal tissues. Research on this subject will involve experts in applied mathematics, computer science, biomedical engineering, computational sciences, and visualization from UT Austin, and in imaging physics from UT M.D. Anderson Cancer Center in Houston. An interdisciplinary research team, consisting of acknowledged leaders in their own fields and with records of collaborative interdisciplinary research, has been assembled to work on the proposed project.
Predictions & Visualizations
Adaptive feedback control of modeling and computational errors and uncertainty using a posteriori error estimates , hierarchical modeling , and adaptive computation on a new component-based computational framework to automate the process of verification and validation of computer simulations.
The project is embedded in the technology of Dynamic Data Driven Application Systems (DDDAS) and involves the development of interfaces between a variety of computational modules and apparatus for experimental mechanics, and computational infrastructure that consists of replaceable code components and the automated parallel code generation process.
Unprecedented Reliability of Computer Simulations: Systematic Validation and Verification of Computer Predictions.