High Spatiotemporal Resolution Ultrasound Imaging Based on Single Plane Wave

The unfocused plane wave transmission mode offers significant advantages in the domain of ultrasound imaging, primarily due to its ultra-high temporal resolution. Unlike traditional focused transmission methods, which send beams one by one, plane wave imaging transmits a broad, unfocused wavefront across the entire imaging field in a single transmission. While this approach enables faster data acquisition by capturing multiple scan lines simultaneously, the excitation of all acoustic sensing points within the imaging field can introduce significant noise and interference from mixed echo signals. This results in poor ultrasound imaging quality with a compromise in spatial resolution. In this study, we aim to integrate deep learning with single-plane-wave acquisition to reconstruct high-quality ultrasound images. Specifically, we have developed a fully neural network trained using self-supervised learning to map single-plane-wave acquisition radio-frequency signals into the low-noise synthetic transmit aperture signals. Subsequently, we utilize the standard delay-and-sum algorithm to reconstruct high-quality ultrasound images. This approach enhances spatial resolution while preserving the ultra-high temporal resolution characteristic of plane-wave ultrasound imaging.