Machine learning-based optimization of a single-element transcranial focused ultrasound transducer for deep brain neuromodulation in mice

Transcranial focused ultrasound is an emerging noninvasive neuromodulation technique that offers high spatial precision and the potential for deep brain penetration. However, due to skull-induced attenuation and acoustic aberrations, precisely stimulating deep brain regions in mice remains challenging. To address this challenge, this study introduces a machine-learning-based computational framework to optimize single-element transducer designs for accurate deep-brain targeting in a mouse model. This framework includes a surrogate model consisting of a Random Forest regressor and classifier, trained on acoustic simulation results to predict performance from design parameters. A total of 72 transducer designs were simulated across coronal and sagittal planes, systematically varying frequency (1-6 MHz), radius of curvature (5-7 mm), and f-number (0.58-1.0). Each design was evaluated using five performance metrics: focal length, focal shape, maximum pressure at the focal region, pressure maximum location, and sidelobe suppression. The surrogate models were then combined with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to perform multi-objective optimization and identify high-performing transducer designs. The optimized design produced a compact, symmetric focal region and accurate energy delivery to deep targets, with minimal off-target exposure, even in complex skull anatomy. Results show that lower f-numbers, moderate radius of curvature, and higher frequencies facilitate precise deep brain targeting. Overall, this data-driven approach enables practical design of single-element transducers for deep-brain neuromodulation in mice and provides a framework for designing transcranial transducers for other brain targets, potentially accelerating the clinical translation of focused ultrasound technologies.