Modeling and optimization of electromagnetic phased arrays for hyperthermia

Progress in the modeling and optimization of phased arrays for electromagnetic hyperthermia is reported. The electromagnetic phased array has the potential to overcome many of the difficulties associated with noninvasive hyperthermia, but is only effective if the driving amplitudes and phases of the array are carefully selected. Several key technologies have been developed in the pursuit of reliably and robustly choosing appropriate driving signals. A computationally efficient method for the optimization of the steady-state temperature distribution, a major driver of therapeutic response, has been developed. By employing a dual set of superposition principles, the technique minimizes the number of computationally expensive forward problems that must be solved in the course of an optimization. A technique for the model-order reduction of the large systems of ordinary differential equations (ODEs) associated with the modeling of transient temperature elevation has been developed. It has been demonstrated that the resultant reduced-order models, despite their considerably reduced computational demands, perform well in optimization and feedback control applications. An additional model-order reduction technique has been developed that is capable of reducing the computational expenses of modeling nonlinear effects in biological heat transfer, such as temperature-dependent blood perfusion rates. Furthermore, a scheme that employs emerging noninvasive tomographic temperature estimation techniques such as magnetic resonance thermometry (MRT) to perform optimization of a phased array has been developed and demonstrated experimentally. Finally, a closed-loop, hybrid proportional-integral-in-time/cost-minimizing-in-space controller has been formulated and its performance demonstrated in simulations. Conclusions about the potential value of each of the developed techniques are reached and directions for further research are indicated.