Effective rejection of the time-varying clutter originating from slowly moving vessels and surrounding tissues is very important for depicting hemodynamics in ultrasound color Doppler imaging. In this paper, a new adaptive clutter rejection method based on Hankel Singular Value Decomposition (Hankel-SVD) is presented for suppressing non-stationary clutter. In the proposed method, a Hankel data matrix is created for each slow-time ensemble. Then the orthogonal principle Hankel components can be obtained through the SVD of the Hankel data matrix. It achieves non-stationary clutter suppression by reconstructing the flow signal with only the high order principle Hankel components, which are estimated from the frequency content carried by the principle Hankel components. To assess its efficiency, the proposed Hankel-SVD based method is applied to synthetic slow-time data obtained from a Doppler flow model and carotid arterial complex baseband data acquired by a commercial ultrasound system (Sonix RP). The resulting flow and power images show that the proposed method outperforms the traditional IIR and polynomials regression filter in attenuation of high intense non-stationary clutter signal. It is also adaptive to highly spatially-varying tissue motion and can automatically select the order of the filter, which leads to improved distinguishing between blood and tissue regions compared to other eigen-based filters.%有效抑制由血管或血管周围组织时变运动引起的非平稳杂波对于提高诊断超声彩色血流成像中血流动力学参数描述的准确性有着极其重要的意义。该文基于奇异值滤波技术提出一种改进的非平稳杂波自适应抑制方法。该方法逐次利用单个慢时多普勒回波采样矢量构建 Hankel 矩阵，然后根据奇异值分解后得到的正交 Hankel 主成份所代表的频域内容，动态选取高阶Hankel主成份重构多普勒血流信号，实现非平稳杂波的有效抑制。为验证算法的有效性，分别对多普勒回波仿真模型合成数据与利用彩色超声设备(Sonix RP)采集的颈动脉血流基带回波信号进行滤波处理，然后采用滞一自相关估计法计算血流平均速度与功率并进行成像。处理结果表明，相对于传统IIR滤波方法与多项式回归滤波技术，利用该文所提算法可对高强度、非平稳杂波进行充分抑制，提高血流估计精度，此外，该算法具有空间自适应性，无需人为设定阈值参数以估计杂波空间维数，与现有基于特征分解的自适应滤波方法相比，可以有效提高组织空间高强度时变运动时血流与组织的区分能力。
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