A Deep Learning Framework for Predicting Ultrasound Array Geometry and Skull Shape in Precision Ultrasound Neuromodulation

Transcranial focused ultrasound stimulation (tFUS) is a promising noninvasive neuromodulation technique that enables targeted brain stimulation with higher precision than other noninvasive methods. However, unknown parameters such as the geometry of the ultrasound (US) phased array (i.e., positions of US elements) particularly in flexible US arrays and variations in skull shape complicate accurate focusing and targeting in US beamforming through electronics. This study presents a deep learning framework using simulated backscattered RF signals to estimate the US array geometry and skull shape. Validated on 5,700 RF simulations, the framework achieved 99.55% training accuracy and 96.84% validation accuracy for array geometry estimation, and Dice scores of 93.99% (training) and 91.15% (validation) for skull segmentation. Testing on unseen datasets demonstrated robust spatial precision, with a mean Euclidean distance of 1.85 mm and strong precision, recall, and F1 scores.