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基于分布式压缩感知的超声仪器测量域自适应波束形成方法:初步开发。

A measurement-domain adaptive beamforming approach for ultrasound instrument based on distributed compressed sensing: Initial development.

机构信息

College of Engineering Shantou University, 515063 Shantou, China.

出版信息

Ultrasonics. 2013 Jan;53(1):255-64. doi: 10.1016/j.ultras.2012.06.009. Epub 2012 Jul 16.

Abstract

High efficient acquisition of the sensor array signals and accurate reconstruction of the backscattering medium are important issues in ultrasound imaging instrument. This paper presents a novel measurement-domain adaptive beamforming approach (MABF) based on distributed compressed sensing (DCS) which seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated with much few measurements under the Nyquist rate. Instead of sampling conventional backscattering signals at the Nyquist rate, few linear projections of the returned signal with random vectors are taken as measurements, which can reduce the amount of samples per channel greatly and makes the real-time transmission of sensor array data possible. Then high resolution ultrasound image is reconstructed from the few measurements of DCS directly by the proposed MABF algorithm without recovering the raw sensor signals with complex convex optimization algorithm. The simulated results show the effectiveness of the proposed method.

摘要

高效获取传感器阵列信号和准确重建背散射介质是超声成像仪器中的重要问题。本文提出了一种基于分布式压缩感知(DCS)的新的测量域自适应波束形成方法(MABF),旨在同时测量在某些域中每个都是稀疏的信号,并且在奈奎斯特率下用很少的测量值进行相互相关。该方法不是以奈奎斯特率对常规回波信号进行采样,而是采用随机向量的回波信号的几个线性投影作为测量值,这可以大大减少每个通道的采样量,并使传感器阵列数据的实时传输成为可能。然后,通过所提出的 MABF 算法,直接从 DCS 的少量测量值重建高分辨率超声图像,而无需使用复杂的凸优化算法恢复原始传感器信号。模拟结果表明了该方法的有效性。

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