Unscented Kalman Filtering for Real Time Thermometry During Laser Ablation Interventions.

We present a data-assimilation Bayesian framework in the context of laser ablation for the treatment of cancer. For solving the nonlinear estimation of the tissue temperature evolving during the therapy, the Unscented Kalman Filter (UKF) predicts the next thermal status and controls the ablation process, based on sparse temperature information. The purpose of this paper is to study the outcome of the prediction model based on UKF and to assess the influence of different model settings on the framework performances.

Model-Based Thermometry for Laser Ablation Procedure Using Kalman Filters and Sparse Temperature Measurements.

Objective: We implement a data assimilation Bayesian framework for the reconstruction of the spatiotemporal profile of the tissue temperature during laser irradiation. The predictions of a physical model simulating the heat transfer in the tissue are associated with sparse temperature measurements, using an Unscented Kalman Filter.

Methods: We compare a standard state-estimation filtering procedure with a joint-estimation (state and parameters) approach: whereas in the state-estimation only the temperature is evaluated, in the joint-estimation the filter corrects also uncertain model parameters (i.