Direct-Necrosis-Monitoring-Based Adaptive Model Predictive Control for Ablation Therapy Including Patient-Specific Residual Heat Management.

Thermal ablation therapy has emerged as a promising strategy for treating tumors that are difficult to resect surgically, such as deep-seated glioblastoma. Recent advances in imaging modalities, particularly photoacoustic and ultrasound imaging, have demonstrated the feasibility of direct necrosis monitoring, offering more accurate assessments than traditional temperature-based methods. Building on these developments, the integration of real-time necrosis feedback (NFB) into ablation control has been introduced. However, several existing NFB control studies neglected the influence of residual heat after ablation is terminated. Furthermore, NFB itself lacks temperature monitoring capabilities, raising key questions regarding how residual heat affects ablation outcomes and how it should be effectively managed. Model predictive control (MPC) offers a potential solution for managing residual heat, but its effectiveness depends on the accurate identification of patient-specific thermal parameters. To address these challenges, we propose a Direct Necrosis-Monitoring-based Adaptive Model Predictive Control (DNaMPC) framework. This method leverages real-time NFB while accounting for residual heat and adaptively identifies patient-specific parameters using a hierarchical Extended Kalman Filter (EKF) architecture. The approach employs a novel parameter absorption strategy, where a single highly observable parameter (thermal damage threshold,