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.