超声相干平面波复合成像的环形统计矢量加权

为提高超声相干平面波复合(CPWC)成像质量,本文提出了基于环形统计矢量(CSV)的加权算法。该算法以延时信号相位为环形统计样本,通过样本平均合矢量建立反映相位分布一致性程度的相干因子。进一步地,根据波束形成及相干因子构建数量的不同,提出了全孔径环形统计矢量(tCSV)加权算法。结果表明,相比于CPWC,CSV和tCSV的散射靶点分辨率和囊肿的对比度分别提高了至少23.67%和27.69%,CNR值降低至多39.37%。与相干因子(CF)和符号相干因子(SCF)相比,虽然CSV和tCSV算法在分辨率和对比度上最大分别比之减小约12.83%和88.31%,但抑制背景噪声和保留目标靶点回波幅值的能力较强,且CNR值比之提高了约20%,其成像质量具有更好地鲁棒性。

Circular statistics vector weighting for ultrasound coherent plane wave compounding

To improve the quality of ultrasonic coherent plane wave compounding ( CPWC) imaging, a weighting algorithm based on circular statistics vector (CSV) is proposed. In this algorithm, the phase of delayed signal is taken as the circular statistical samples and the coherence factor reflecting the consistency of phase distribution is established through the sample average resultant vector. Furthermore, according to the different number of beamforming and coherence factor construction, the weighting algorithm of total circular statistics vector (tCSV) is proposed. Compared with CPWC, results show that the scattering target resolution and cyst contrast radio of CSV and tCSV are increased by at least 23. 67% and 27. 69% , respectively. And the CNR value is decreased by up to 39. 37% . Compared with the coherence factor (CF) and the sign coherence factor (SCF), the maximum resolution and contrast of CSV and tCSV algorithms are reduced by about 12. 83% and 88. 31% , respectively. However, the ability to suppress background noise and retain the amplitude of target echo wave is better. The CNR value is improved by about 20% , and the image quality is more robust.