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In hyperthermia for cancer treatments, tumors are heated to improve the outcome of radio-and chemotherapies. Using extracorporeal high-intensity focused ultrasound (HIFU) transducers, the heating can be applied noninvasively, accurately, and with high acoustic power. The heating location can be changed by electronic beam steering, within the transducer’s small local heating range, and by mechanically repositioning the transducer, which effectively shifts the local range to enable the treatment of larger tumors. An important problem is the selection of the sonication points, i.e., the discrete locations reachable by electronic beam steering, and the admissible mechanical transducer positions, which must be performed prior to treatment, and can be seen as an actuator selection problem. Clearly, the selected sonication points and transducer positions have a major impact on the achievable tumor temperatures, and their number is directly related to the controller’s complexity and, thereby, real-time feasibility. To address this challenging problem, this brief presents a target-conformal optimization-based actuator placement procedure, designed to assist the clinician in enabling maximum treatment quality using only a limited number of sonication points and transducer positions. The method is computationally tractable, and takes into account the specific tumor geometry and tissue properties.

Purpose: In local hyperthermia, precise temperature control throughout the entire target region is key for swift, safe, and effective treatment. In this article, we present a model predictive control (MPC) algorithm providing voxel-level temperature control in magnetic resonance-guided high intensity focused ultrasound (MR-HIFU) and assess the improvement in performance it provides over the current state of the art. Materials and methods: The influence of model detail on the prediction quality and runtime of the controller is evaluated and a tissue mimicking phantom is characterized using the resulting model. Next, potential problems arising from modeling errors are evaluated in silico and in the characterized phantom. Finally, the controller’s performance is compared to the current state-of-the-art hyperthermia controller in side-by-side experiments. Results: Modeling diffusion by heat exchange between four neighboring voxels achieves high predictive performance and results in runtimes suited for real-time control. Erroneous model parameters deteriorate the MPC’s performance. Using models derived from thermometry data acquired during low powered test sonications, however, high control performance is achieved. In a direct comparison with the state-of-the-art hyperthermia controller, the MPC produces smaller tracking errors and tighter temperature distributions, both in a homogeneous target and near a localized heat sink. Conclusion: Using thermal models deduced from low-powered test sonications, the proposed MPC algorithm provides good performance in phantoms. In direct comparison to the current state-of-the-art hyperthermia controller, MPC performs better due to the more finely tuned heating patterns and therefore constitutes an important step toward stable, uniform hyperthermia.