Compensated Row-Column Ultrasound Imaging Systems with Data-Driven Point Spread Function Learning

摘要

Ultrasound imaging systems are invaluable tools used in applications ranging from medical diagnostics to non-destructive testing. The concept of row-column imaging using row-column-addressed arrays has received a lot of attention recently for 3-D ultrasound imaging. However, it suffers from a few intrinsic limitations: data sparsity, speckle noise, and a spatially varying point spread function. These limitations cannot be addressed by transducer design alone. In this research, we propose PL-UIS, a compensated ultrasound imaging system that combines physical modeling with data-driven spatially varying point spread function learning within a random field framework to address the limitations of row-column ultrasound imaging. Experimental results using the proposed ultrasound imaging system show the effectiveness of our proposed PL-UIS system compared to state-of-the-art compensated ultrasound imaging systems.