A Deep Reinforcement Learning Based Region-Specific Beamformer for Sparse Arrays 3-D Ultrasound Imaging

Abstract

Sparse arrays offer several advantages over other element reduction techniques for 3-D ultrasound imaging. However, the large interelement spacing in these arrays results in high sidelobe-related artifacts, which significantly degrade image quality and limit their application in 3-D ultrasound imaging. Adaptive beamformers have been proposed to mitigate sidelobe-related artifacts, but they often degrade speckle texture quality, resulting in unnaturally dark images. To overcome these limitations, we propose RSB-Net, a region-specific beamformer based on deep reinforcement learning (DRL). RSB-Net adaptively selects the most suitable beamformer for each pixel of the image, applying adaptive beamforming in regions dominated by sidelobe artifacts and delay-and-sum (DAS) beamforming in regions where speckle texture should be preserved. The effectiveness of RSB-Net was validated on both simulated and experimental synthetic transmit aperture (STA) RF datasets with a newly designed sparse array prototype. On simulated data, RSB-Net achieved significant gains, with improvements of 52.81 dB in contrast ratio (CR) and 0.65 in a generalized contrast-to-noise ratio (gCNR) compared to DAS beamforming. In experimental tissue-mimicking phantom data, RSB-Net demonstrated similar performance, achieving gains of 51.01 dB and 0.64, respectively. These results highlight the potential of RSB-Net as a robust and effective solution for high-quality B-mode 3-D ultrasound imaging using 2-D sparse arrays, advancing the standardization of 3-D ultrasound in clinical settings by enhancing anatomical visualization, reducing operator dependency, and improving measurement accuracy for lesions and calcifications.