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Abstract #2938

Generalized Polynomial Chaos as a Uncertainty Quantification Method for Modeling MR Guided Laser Induced Thermal Therapy.

Samuel John Fahrenholtz1, 2, David Fuentes1, John D. Hazle1, 2, Roger Jason Stafford1, 2

1Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States; 2The University of Texas Graduate School of Biomedical Science, Houston, TX, United States

Thermodynamics modeling in the brain could improve the speed and efficacy of ablative thermal therapies in the brain. The addition of generalized polynomial chaos to thermodynamics modeling provides uncertainty quantification, e.g. a mean and standard deviation for a predicted treatment temperature. This provides confidence intervals to the prediction that can be compared to MR thermal imaging from water proton frequency shift. This abstract retrospectively compares MR thermal imaging from a canine brain ablation with a finite element approach to the Pennes bioheat equation; uncertainty quantification is provided by generalized polynomial chaos.