Computational and MR-guided Patient-Specific Laser Induced Thermal Therapy of Cancer

This chapter describes the development of a canonical dynamic data driven predictive control system for MR-guided laser induced thermal therapies (MRgLITT) of focal cancerous lesions within soft tissue. The predictive ability of computational models combined with advanced clinical imaging modalities is exploited to plan, predict, control, and optimize the treatment outcome. The system is under continual development and embodies a cyberinfrastructure comprised of Magnetic Resonance Thermal Imaging (MRTI), computer visualization, laser optics, high-speed networks, nonlinear dynamic bioheat transfer models of heterogeneous tissue, adaptive meshing, high-performance parallel computing, cell-damage models, inverse analysis, calibration, model validation, signal processing, optimal control algorithms, and error estimation and control. These diverse technologies and systems are connected across a high-speed computational grid connecting remote sites 150 miles apart and is an excellent example of a Dynamic Data Driven Application System (DDDAS). Webpage: http://wiki.ices.utexas.edu/dddas A. Elliott, D. Fuentes, J. D. Hazle, A. Shetty, and R. J. Stafford, The University of Texas M.D. Anderson Cancer Center, Department of Imaging Physics, Houston TX 77030, USA, e-mail: [andrew.elliott,dtfuentes,jhazle,anil.shetty, jstafford]@mdanderson.org Y. Feng, Computational Bioengineering and Nanotechnology Lab, The University of Texas at San Antonio, San Antonio, TX 78749, USA, e-mail: yusheng.feng@utsa.edu K. R. Diller Department of Biomedical Engineering, The University of Texas at Austin, Austin TX 78712, USA, e-mail: oden@ices.utexas.edu,kdiller@mail.utexas.edu J. T. Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin TX 78712, USA, e-mail: oden@ices.utexas.edu