Evolutionary Optimization of a Focused Ultrasound Propagation Predictor Neural Network

Evolutionary Optimisation, Evolutionary Design, Ultrasound Propagation Predictor, Cartesian Genetic Programming

The search for the optimal treatment plan of a focused ultrasound based procedure is a complex multi-modal problem, trying to deliver the solution in clinically relevant time while not sacrificing the precision bellow a critical threshold. To test a solution, a multitude of computationally expensive simulations need to be evaluated, often thousands of times. Recent renaissance of machine learning could provide a solution to this. Indeed, a state-of-the-art neural predictor of the Acoustic Propagation through a human skull was published recently, speeding up the simulation significantly. The utilized architecture, however, could use some improvements in precision. To explore their design more deeply, we made an attempt to improve the solver by use of an evolutionary algorithm, challenging the importance of different building blocks. Utilizing Genetic Programming, we managed to improve their solution significantly, resulting in a solver with approximately an order of magnitude better RMSE of the predictor, while still delivering solutions in reasonable time frame. Furthermore, a second study was conducted to gauge the effects of the multi-resolution encoding on precision of the network, providing interesting topics for further research on the effects of the memory blocks and convolution kernel sizes for PDE RCNN solvers.