A CNN-Based Measurement Signals Transformation Method for High-Quality Apodized Plane Wave Ultrasound Imaging

Plane wave (PW) transmission enables ultrahigh-frame-rate imaging by insonifying the entire field of view with a full-array excitation. However, because PW transmission lacks transmit focusing, the echo signals suffer from low signal-to-noise ratio, which in turn degrades the overall image quality. In contrast, synthetic transmit aperture (STA) imaging uses single-element transmission, which reduces interference and noise in the measured echoes. Combined with full-aperture reception and dynamic focusing during image reconstruction, STA enables high-quality imaging. However, STA requires as many sequential transmissions as the number of probe elements, resulting in a significantly reduced frame rate due to the prolonged acquisition time. In this context, we propose designing a convolutional neural network (CNN) to map the apodized PW measurement signals to their STA-equivalent counterparts, aiming to preserve the high frame rate of PW imaging while achieving image quality comparable to that of STA. Specifically, based on the superposition principle of linear acoustics, we first establish a mathematical measurement relationship between PW and STA signals. This theoretical model then guides the design of a CNN for measurement signal transformation. Finally, high-resolution B-mode ultrasound images are reconstructed using the delay-and-sum beamforming algorithm. We trained the network via self-supervised learning on a simulated dataset generated by Field II and assessed its performance through both in-vitro and in-vivo experiments. The results demonstrate the feasibility and effectiveness of using a CNN to generate STA measurement signals from apodized PW measurement signals, thereby advancing ultrasound imaging by combining high frame rates with improved image quality. Notably, the proposed method exhibits excellent performance in both lateral and axial (longitudinal) resolution. Overall, this study presents a promising approach for achieving high spatiotemporal resolution in ultrasound imaging based on apodized PW acquisition.