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首页> 外文会议>National Conference on Biomedical Engineering;International Iranian Conference on Biomedical Engineering >Adaptive beamforming with automatic diagonal loading in medical ultrasound imaging
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Adaptive beamforming with automatic diagonal loading in medical ultrasound imaging

机译：超声自动成像中具有自动对角线加载的自适应波束形成

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In medical ultrasound imaging, the most famous adaptive beamforming algorithm is the minimum variance (MV). Diagonal loading (DL) method is usually used to improve the robustness of the MV. Conventional DL methods have a critical problem which is determining the loading factor ignoring the input data. To address this problem, in this paper, using the shrinkage algorithm is proposed in which the loading coefficient is completely automated and determined by the input data. The performance of the proposed algorithm is evaluated by simulated ultrasound data in Field II. In point targets simulation, it has been shown that the proposed method improves the resolution about 87% and 20%, compared to delay and sum (DAS) and MV algorithms (with a fixed loading coefficient of), respectively. In addition, in anechoic cyst simulation, the contrast and relative contrast of the proposed method has been retained, in comparison to those of the MV beamformer, while they are improved about 5% and 18%, in comparison to the DAS ones, respectively.
机译：在医学超声成像中，最著名的自适应波束形成算法是最小方差（MV）。通常使用对角加载（DL）方法来提高MV的鲁棒性。常规的DL方法有一个关键问题，那就是忽略输入数据而确定加载因子。为了解决这个问题，在本文中，提出了使用收缩算法，在该算法中，加载系数是完全自动化的，并由输入数据确定。通过在场II中模拟的超声数据评估了所提出算法的性能。在点目标仿真中，已经表明，与延迟和总和（DAS）和MV算法（具有固定的加载系数）相比，该方法将分辨率提高了约87％和20％。此外，在无回声囊肿模拟中，与MV波束形成器相比，该方法的对比度和相对对比度得以保留，而与DAS相比，分别提高了5％和18％。

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