SU‐E‐J‐161: Inverse Problems for Optical Parameters in Laser Induced Thermal Therapy

Abstract

Purpose:Magnetic resonance‐guided laser‐induced thermal therapy (MRgLITT) is investigated as a neurosurgical intervention for oncological applications throughout the body by active post market studies. Real‐time MR temperature imaging is used to monitor ablative thermal delivery in the clinic. Additionally, brain MRgLITT could improve through effective planning for laser fiber's placement. Mathematical bioheat models have been extensively investigated but require reliable patient specific physical parameter data, e.g. optical parameters. This abstract applies an inverse problem algorithm to characterize optical parameter data obtained from previous MRgLITT interventions.Methods:The implemented inverse problem has three primary components: a parameter‐space search algorithm, a physics model, and training data. First, the parameter‐space search algorithm uses a gradient‐based quasi‐Newton method to optimize the effective optical attenuation coefficient, μ_eff. A parameter reduction reduces the amount of optical parameter‐space the algorithm must search. Second, the physics model is a simplified bioheat model for homogeneous tissue where closed‐form Green's functions represent the exact solution. Third, the training data was temperature imaging data from 23 MRgLITT oncological brain ablations (980 nm wavelength) from seven different patients.Results:To three significant figures, the descriptive statistics for μ_eff were 1470 m−1 mean, 1360 m−1 median, 369 m−1 standard deviation, 933 m−1 minimum and 2260 m−1 maximum. The standard deviation normalized by the mean was 25.0%. The inverse problem took <30 minutes to optimize all 23 datasets.Conclusion:As expected, the inferred average is biased by underlying physics model. However, the standard deviation normalized by the mean is smaller than literature values and indicates an increased precision in the characterization of the optical parameters needed to plan MRgLITT procedures. This investigation demonstrates the potential for the optimization and validation of more sophisticated bioheat models that incorporate the uncertainty of the data into the predictions, e.g. stochastic finite element methods.