A Feasibility Study on Acoustic Focusing Simulation for tFUS Using Conditional Generative Adversarial Network

Transcranial focused ultrasound (tFUS) enables the transmission of acoustic energy through the skull to converge onto minute brain regions. Numerical simulations using the K-Wave toolbox offer valuable insights into ultrasound wave propagation and focusing. However, prohibitively slow simulation speeds impede real-time visualization and analysis of tFUS. Here, in this paper, a conditional generative adversarial network (cGAN) framework has been applied to predict the acoustic field generated by tFUS phased arrays. A transcranial acoustic field dataset was generated via the K-Wave toolbox, simulating the transmission of a 128-element spherical phased array through a human skull model reconstructed from CT scans. Key parameters, including pressure distribution, focus size, and phase delay, were computed. A cGAN architecture was trained to directly map skull geometry and transducer parameters to the intracranial sound field, thereby avoiding time-consuming numerical predictions. The calculation results demonstrate that cGAN-driven simulations can predict acoustic field characteristics effectively, including focal shift-related errors stemming from skull-induced distortions.