Advancing Single-Plane Wave Ultrasound Imaging With Implicit Multiangle Acoustic Synthesis via Deep Learning

Abstract

Plane wave imaging (PWI) is pivotal in medical ultrasound (US), prized for its ultrafast capabilities essential for real-time physiological monitoring. Traditionally, enhancing image quality in PWI has necessitated an increase in the number of plane waves (PWs), unfortunately compromising its hallmark high frame rates. To fully leverage the frame rate advantage of PWI, existing deep-learning-based methods often use single-PW as the sole input for training strategies to replicate multi-PWs compounding results. However, these typically fail to capture the intricate information provided by steered waves. In response, we have developed a sophisticated architecture that implicitly integrates multiangle information by generating and dynamically combining virtual steered PWs within the network. Using deep learning (DL) techniques, this system creates virtual steered waves from the single primary input view, simulating a limited number of steering angles. These virtual PWs are then expertly merged with actual single-PW data through an advanced attention mechanism. Through implicit multiangle acoustic synthesis, our approach achieves the high-quality output typically associated with extensive multiangle compounding. Rigorously evaluated on datasets acquired from simulations, experimental phantoms, and in vivo targets, our method has demonstrated superior performance over traditional single-PW strategies by providing more stable, reliable, and robust imaging outcomes. It excels in restoring detailed speckle patterns and diagnostic characteristics crucial for in vivo imaging, thereby offering a promising advancement in PWI technology without sacrificing speed. The code of the network is publicly available at https://github.com/yijiaLiu12/Implicit-Plane-Wave-Synthesis.