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用于传统聚焦波束超声成像系统的最优加权非线性波束形成器。

Optimally-weighted non-linear beamformer for conventional focused beam ultrasound imaging systems.

机构信息

Biomedical Ultrasound Laboratory, Department of Applied Mechanics, Indian Institute of Technology, Madras, Chennai, India.

出版信息

Sci Rep. 2021 Nov 3;11(1):21622. doi: 10.1038/s41598-021-00741-5.

Abstract

A novel non-linear beamforming method, namely, filtered delay optimally-weighted multiply and sum (F-DowMAS) beamforming is reported for conventional focused beamforming (CFB) technique. The performance of F-DowMAS was compared against delay and sum (DAS), filtered delay multiply and sum (F-DMAS), filtered delay weight multiply and sum (F-DwMAS) and filter delay Euclidian weighted multiply and sum (F-DewMAS) methods. Notably, in the proposed method the optimal adaptive weights are computed for each imaging point to compensate for the effects due to spatial variations in beam pattern in CFB technique. F-DowMAS, F-DMAS, and DAS were compared in terms of the resulting image quality metrics, Lateral resolution (LR), axial resolution (AR), contrast ratio (CR) and contrast-to-noise ratio (CNR), estimated from experiments on a commercially available tissue-mimicking phantom. The results demonstrate that F-DowMAS improved the AR by 57.04% and 46.95%, LR by 58.21% and 53.40%, CR by 67.35% and 39.25%, and CNR by 44.04% and 30.57% compared to those obtained using DAS and F-DMAS, respectively. Thus, it can be concluded that the newly proposed F-DowMAS outperforms DAS and F-DMAS. As an aside, we also show that the optimal weighting strategy can be extended to benefit DAS.

摘要

一种新的非线性波束形成方法,即滤波延迟最优加权乘法和求和(F-DowMAS)波束形成,被报道用于传统的聚焦波束形成(CFB)技术。F-DowMAS 的性能与延迟求和(DAS)、滤波延迟乘法和求和(F-DMAS)、滤波延迟加权乘法和求和(F-DwMAS)和滤波延迟欧几里得加权乘法和求和(F-DewMAS)方法进行了比较。值得注意的是,在提出的方法中,为每个成像点计算最佳自适应权重,以补偿 CFB 技术中波束图案空间变化的影响。F-DowMAS、F-DMAS 和 DAS 在图像质量指标、横向分辨率(LR)、轴向分辨率(AR)、对比比(CR)和对比噪声比(CNR)方面进行了比较,这些指标是通过在商用组织模拟体模上进行的实验来估计的。结果表明,与 DAS 和 F-DMAS 相比,F-DowMAS 分别将 AR 提高了 57.04%和 46.95%,LR 提高了 58.21%和 53.40%,CR 提高了 67.35%和 39.25%,CNR 提高了 44.04%和 30.57%。因此,可以得出结论,新提出的 F-DowMAS 优于 DAS 和 F-DMAS。此外,我们还表明,最优加权策略可以扩展到 DAS 中受益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c07c/8566575/c8fa862937d1/41598_2021_741_Fig1_HTML.jpg

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