Computational modeling of a single-element transcranial focused ultrasound transducer for subthalamic nucleus...

While transcranial focused ultrasound is a very promising neuromodulation technique for its non-invasiveness and high spatial resolution, its application to the human deep brain regions such as the subthalamic nucleus (STN) is relatively new. The objective of this study is to design a simple ultrasound transducer and study the transcranial wave propagation through a highly realistic human head model. The effects of skull morphology and skull and brain tissue properties on the focusing performance and energy deposition must therefore be known.

A full-wave finite-difference time-domain simulation platform was used to design and simulate ultrasound radiation from a single-element focused transducer (SEFT) to the STN. Simulations were performed using the state-of-the-art Multimodal Imaging-based and highly Detailed Anatomical (MIDA) head model. In addition, the impact of changes in sound speed, density, and tissue attenuation coefficients were assessed through a sensitivity analysis.

A SEFT model was designed to deliver an intensity of around 100 [Formula: see text] to the STN region; 20% of the STN volume was sonicated with at least half of the maximum of the peak intensity and it was predicted that 61.5% of the volume of the beam (above half of the peak intensity) falls inside the STN region. The sensitivity analysis showed that the skull's sound speed is the most influential acoustic parameter, which must be known with less than 1.2% error to obtain an acceptable accuracy in intracranial fields and focusing (for less than 5% error).

Ultrasound intensity delivery at the STN by a simple single element transducer is possible and could be a promising alternative to complex multi-element phased arrays, or more general, to invasive or less focused (non-acoustic) neuromodulation techniques. Accurate acoustic skull and brain parameters, including detailed skull geometry, are needed to ensure proper targeting in the deep brain region.