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. In particular, we analyze the effects of the time resolution of the filter and the number and the location of the observations. Clinical Relevance - The application of a data-assimilation approach based on limited temperature information allows to monitor and predict in real-time the thermal effects induced by thermal therapy for tumors.