基于稀疏脉冲采样的低复杂度血流速度估计算法

双模超声广泛用于医学临床诊断，其中B模式脉冲用于成像，多普勒脉冲则用于血流速度估计。数据采集时间在两种模式之间共享。为了提高B模式图像的更新频率，需要减少多普勒脉冲数量，即发射稀疏多普勒脉冲进行血流速度估计。然而现有的适应稀疏脉冲采样算法，如迭代自适应算法、稀疏贝叶斯法以及基于阵列虚拟拓展的子空间类方法，计算开销巨大，难以满足实时成像的要求，且在稀疏度大的情况下会产生明显的伪影。为此，文中提出了一种基于稀疏脉冲采样的低复杂度血流速度估计算法。根据超声多普勒回波信号是由血红细胞的散射产生，具有强相干、信源个数时变的特点，文中首先从子空间角度解析了伪影的成因，并验证了包含均匀脉冲的稀疏发射脉冲排布方式可以有效地抑制伪影；然后以均匀脉冲回波构建协方差矩阵，并进行空间平滑获取特征值，以较大特征值的个数和相互的比值作为标准，判断血流不同时刻的频率分布特征；最后以此频率分布特征为标准，自适应采用B-MUSIC算法或TBVAM算法进行血流速度估计，以降低算法的复杂度。Matlab仿真和人体实测数据的实验结果表明，该算法在极大地减小计算复杂度的同时，可以获得较为连续、清晰且伪影抑制效果较佳的血流速度估...

Low Complexity Blood Flow Velocity Estimation Algorithm via Sparse Pulse Sampling

Dual-mode ultrasound is widely used in medical clinical diagnosis. The B-mode pulse is used for imaging and Doppler pulse is used for blood flow velocity estimation. The data collection time is shared between the two modes. To improve the update frequency of B-mode image, it is necessary to reduce the number of Doppler pulses, that is, to estimate the blood flow velocity by sparse Doppler emissions. However, the existing algorithms for sparse pulse sampling, such as iterative adaptive algorithm, sparse Bayesian algorithm and subspace method based on array virtual expansion, are huge in expense and can not meet the requirements of real-time imaging. What’s more, they will lead to obvious artifacts in the case of large sparsity. Therefore, this paper proposed a low complexity blood flow velocity estimation algorithm via sparse pulse sampling. Based on the fact that ultrasonic Doppler echo signal is generated by the scattering of red blood cells, so echoes are strong coherence signals with time-variation sources number, this paper firstly explained the cause of artifacts from the perspective of subspace, and verified that the sparse emission pulse arrangement with uniform pulse can effectively suppress artifacts. Then the covariance matrix was constructed with uniform pulse echo, and the eigenvalues were obtained after spatial smoothing. The frequency distribution characteristics of blood flow at different segments were derived by the number of larger eigenvalues and the ratio of each other. Finally, based on the frequency distribution characteristics, the B-MUSIC algorithm or TBVAM algorithm was adaptively used for blood flow velocity estimation to reduce the complexity of the algorithm. The experimental results with Matlab simulation and human body measurement data show that the algorithm can obtain continuous, clear blood flow velocity estimation results with well artifact suppression while reducing the computational complexity significantly.