Cross Fusion and Correlation Beamformer for Row-Column Array Based 3D Ultrasound Imaging
Qiandong Sun, Rui He, Shilin Hou, Jiyan Dai, and Kailiang Xu

TL;DR
This paper introduces a novel cross fusion and correlation (CFAC) method for RCA-based 3D ultrasound imaging that significantly reduces sidelobe artifacts and noise, enhancing image quality and microvascular visualization.
Contribution
The paper presents the CFAC technique, which leverages incoherence across datasets to improve 3D ultrasound imaging quality in RCA transducers, outperforming existing methods.
Findings
CFAC reduced sidelobe levels by up to 42 dB in simulations.
CFAC improved CNR by up to 17.5 dB in phantom experiments.
CFAC enabled detailed microvascular visualization in vivo.
Abstract
Row column addressed (RCA) transducers present a promising solution for ultrafast volumetric imaging with a reduced channel count and a large field of view. However, RCA-based 3D imaging is fundamentally limited by severe sidelobe artifacts and a low signal-to-noise ratio (SNR), primarily due to weak transmit focusing inherent in RCA based ultrafast imaging strategies. To overcome these challenges, we propose a cross fusion and correlation (CFAC) method that leverages the incoherence of sidelobe artifacts and noise across datasets acquired using orthogonal apertures and multiple steering angle sets. The performance of the proposed method was validated through simulations, in vitro imaging of a multi-purpose ultrasound phantom, and in vivo experiments, and benchmarked against four established techniques: orthogonal plane wave (OPW) imaging, XDoppler method, row-column-specific…
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