Compressed sensing (CS) has been adapted to synthetic aperture (SA) ultrasound imaging to improve the frame-rate of the system. Recently, we proposed a novel CS framework using Gaussian under-sampling to reduce the number of receive elements in multi-element synthetic transmit aperture (MSTA) imaging. However, that framework requires different receive elements to be chosen randomly for each transmission, which may add to practical implementation challenges. Modifying the scheme to employ the same set of receive elements for all transmissions of MSTA leads to degradation of the recovered image quality. Therefore, this work proposes a novel sampling scheme based on a genetic algorithm (GA), which optimally chooses the receive element positions once and uses it for all the transmission of MSTA. The CS performance using GA sampling schemes is evaluated against the previously proposed CS framework on in-vitro and in-vivo datasets. The obtained results suggest that not only does the GA-based approach allows the use of the same set of sparse receive elements for each transmit, but also leads to the lowest CS recovery error (NRMSE) and 14% overall improvement in image contrast, in comparison to the previously-proposed Gaussian sampling scheme. Thus, using the CS framework along with GA, can potentially reduce the complexity in implementation of CS-framework to MSTA based systems.

中文翻译：

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多元素合成传输孔径成像的压缩传感框架的实际实施

压缩传感 (CS) 已适用于合成孔径 (SA) 超声成像，以提高系统的帧速率。最近，我们提出了一种使用高斯欠采样的新型 CS 框架，以减少多元件合成发射孔径 (MSTA) 成像中的接收元件数量。然而，该框架需要为每次传输随机选择不同的接收元素，这可能会增加实际实现的挑战。修改该方案以对 MSTA 的所有传输使用相同的一组接收元件会导致恢复的图像质量下降。因此，这项工作提出了一种基于遗传算法 (GA) 的新型采样方案，该方案对接收元件位置进行一次优化选择，并将其用于 MSTA 的所有传输。使用 GA 采样方案的 CS 性能根据先前提出的体外和体内数据集的 CS 框架进行评估。获得的结果表明，基于 GA 的方法不仅允许为每个传输使用相同的稀疏接收元素集，而且还导致最低的 CS 恢复误差 (NRMSE) 和 14% 的图像对比度整体改进，在与之前提出的高斯采样方案的比较。因此，将 CS 框架与 GA 一起使用，可以潜在地降低将 CS 框架实施到基于 MSTA 的系统的复杂性。但与之前提出的高斯采样方案相比，还导致最低的 CS 恢复误差 (NRMSE) 和 14% 的图像对比度整体改进。因此，将 CS 框架与 GA 一起使用，可以潜在地降低将 CS 框架实施到基于 MSTA 的系统的复杂性。但与之前提出的高斯采样方案相比，还导致最低的 CS 恢复误差 (NRMSE) 和 14% 的图像对比度整体改进。因此，将 CS 框架与 GA 一起使用，可以潜在地降低将 CS 框架实施到基于 MSTA 的系统的复杂性。