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中文核心期刊

融合最优变迹函数和多参数复合条件系数的超声成像算法

An ultrasound imaging algorithm combining optimal apodization function and multi-parameter composite conditional factor

  • 摘要: 单一参数的相干系数难以在复杂成像场景中同时提高超声图像的分辨率和对比度, 为此提出了一种融合最优变迹函数和多参数复合条件系数的超声成像算法以提高图像的综合质量。首先, 对接收的阵列回波信号进行多组变迹窗函数的加权, 采用波束形成器输出方差最小的原则选择最优窗函数。其次, 将选择的变迹窗函数加权到回波信号中得到优化后的阵列信号, 并且利用多采样时刻的信号计算当前的条件相干性。最后, 根据全局图像相干性信息对多组参数下的条件系数进行复合得到最终的加权系数。仿真和实验结果表明, 相较于传统的延时叠加算法, 本文提出的算法可以明显提高图像分辨率和对比度, 其半峰全宽值在点目标仿真中最大改善了89.23%, 在颈动脉纵切面实验中最大改善了63.22%。相较于传统的相干系数算法, 所提算法在保持图像对比度的同时提高了分辨率性能。

     

    Abstract: The coherence factor of a single parameter is difficult to simultaneously improve the resolution and contrast of ultrasound images in complex imaging scenarios. Therefore, an ultrasound imaging algorithm combining optimal apodization function and multi-parameter composite conditional factor (OA-MCCF) is proposed to improve the overall image quality. Firstly, several sets of apodization window functions are weighted on the received echo signals, and the optimal apodization function is selected based on the principle of minimizing the variance of the beamformer output. Secondly, the selected apodization function is weighted onto the echo signals to obtain the optimized array signals, and the current conditional coherence is calculated by the signals at multiple sampling times. Finally, the weighted factor is obtained by composing the conditional factors with multiple parameters according to the global image coherence information. The simulated and experimental results demonstrate that, compared with the traditional delay-and-sum (DAS) algorithm, the proposed OA-MCCF algorithm can significantly enhance image resolution and contrast. Specifically, the OA-MCCF can achieve the greatest improvements in full width at half maximum (FWHM) of 89.23% in the point-target simulation and 63.22% in the carotid longitudinal-section experiment. Compared with the traditional coherence factor algorithm, the proposed algorithm improves resolution performance while maintaining image contrast.

     

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