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利用相位相干系数(PCF)和广义相干系数(GCF)对波束形成后的结果进行加权,能有效提高超声成像的质量,但存在背景组织亮度降低,对比度不高,以及远处目标成像强度降低等问题。本文提出一种基于次方样本熵的合成孔径成像算法,将单个孔径发射时的低质量成像结果作为元素,根据孔径位置排列,构成空间向量。根据不同成像点对应的空间向量的随机性不同,计算每个点的空间向量的次方样本熵,并将该熵值作为权系数进行加权成像。采用FieldⅡ仿真数据成像结果表明,相比于传统的DAS算法,次方样本熵方法能够提高成像的分辨率和对比度;相比于PCF和GCF算法,次方样本熵方法能够在不损失组织背景强度的情况下,进一步改善了成像质量。
Weighting the beamforming results with phase coherence (PCF) and generalized coherence (GCF) can effectively improve the quality of ultrasound imaging, but there is a decrease in background tissue brightness, low contrast, and decreased target imaging intensity at a distance problem. In this paper, a synthetic aperture imaging algorithm based on the entropy of the quadratic samples is proposed. The low-quality imaging results when a single aperture is launched are used as elements, and the spatial vectors are arranged according to the positions of the apertures. According to the randomness of the space vector corresponding to different imaging points, the square sample entropy of the space vector of each point is calculated, and the entropy value is weighted as the weighting coefficient. Compared with the traditional DAS algorithm, the quadratic sample entropy method can improve the resolution and contrast of the imaging. Compared with the PCF and GCF algorithms, the quadratic sample entropy method can not damage the background intensity of the tissue The image quality is further improved.