A deep-learning model for one-shot transcranial ultrasound simulation and phase aberration correction

Abstract

Transcranial ultrasound (TUS) has emerged as a promising tool in clinical and research settings due to its potential to modulate neuronal activity, open the blood-brain barrier, facilitate targeted drug delivery via nanoparticles, and perform thermal ablation, all non-invasively. By delivering focused ultrasound waves to precise regions anywhere in the brain, TUS enables targeted energy deposition and is being explored in over fifty clinical trials as a treatment for conditions such as opioid addiction, Alzheimer's disease, dementia, epilepsy, and glioblastoma. However, effective TUS treatment requires careful ultrasound parameter design and precise computation of the focal spot's location and pressure, as skull heterogeneity increases the risk of off-target sonication or insufficient energy delivery to neural tissue. In clinical settings, this phase aberration correction must be computed within seconds. To achieve this, commercial devices often rely on faster methods, such as ray tracing, to predict the focus location and pressure. While computationally efficient, these methods may not always provide the high level of accuracy needed for optimal TUS delivery. We present TUSNet, the first end-to-end deep learning approach to solve for both the pressure field and phase aberration corrections without being bound to the inherent trade-off between accuracy and efficiency. TUSNet computes the 2D transcranial ultrasound pressure field and phase corrections within 21 milliseconds (over $1200\times$ faster than k-Wave, a MATLAB-based acoustic simulation package), achieving $98.3\%$ accuracy in estimating peak pressure magnitude at the focal spot with a mean positioning error of only $0.18$ mm compared to ground truth from k-Wave.