The Impact of Uncertainty in Nonlinear Temperature Dependent Constitutive Parameters on Predictive Computer Modeling of MRgLITT Procedures

David T. A. Fuentes¹, Samuel J. Fahrenholtz², Anil Shetty³, Roger J. McNichols³, Jeffrey S. Weinberg², John D. Hazle², Jason Stafford²

Significant efforts are ongoing to incorporate predictive prospective computer simulation into MRgLITT procedures. Truly predictive prospective computer modeling requires substantial validation efforts and novel computer modeling techniques that incorporate the uncertainty of the input of computer model parameters. Statistical methods provide novel methodologies for modeling the complex bioheat transfer phenomena. Within the probabilistic setting of uncertainty quantification (UQ), the range of constitutive nonlinearities may be modeled through the uncertainty within the linear UQ problem. This novel modeling techniques facilitates a substantial increase in computational efficiency while maintaining the predictability in the computer modeling by incorporating the advanced bioheat transfer phenomena.