Abstract
Abstract Ultrasound imaging, valued for its non-invasiveness and cost-effectiveness, faces challenges in brain imaging due to acoustic impedance differences. This study introduces PEN-UBT, a deep learning-based method, combining Convolutional Neural Network for Forward Propagation (CN-FP) and Subnetwork for Inversion (SNI). CN-FP simplifies wavefield calculations, while SNI facilitates mapping from wavefield to model. PEN-UBT achieves high-fidelity imaging of the skull and soft tissues, excelling in scenarios with varying thrombus velocities. It demonstrates exceptional tissue resolution in brain slices, reducing imaging time to 1.13 seconds. PEN-UBT’s success extends its applicability beyond brain imaging, contributing to the broader field of medical imaging technologies.
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