Ultrafast ultrasound coded vector Doppler imaging of blood flow velocity and resistivity

<sec>Dynamic and precise measurement of cerebral blood flow velocity plays a critical role in neuroscience and the diagnosis of cerebrovascular diseases. Traditional color Doppler ultrasound can only measure the velocity component along the ultrasound beam, thus limiting its ability to accurately capture the full blood flow vector in complex environments. To break through these limitations, we propose an ultrafast pulse-coded vector Doppler (PC-UVD) imaging method, by using Hadamard matrix pulse encoding to enhance velocity estimation accuracy in low signal-to-noise ratio (SNR) conditions. Our study includes spiral flow simulations and in vivo rat brain experiments, which demonstrate significantly improved measurement precision compared with traditional ultrafast vector Doppler (UVD). This novel approach can measure dynamic cerebral blood flow velocity within a single cardiac cycle, presenting insights into cerebrovascular resistivity characteristics.</sec><sec>The proposed PC-UVD method encodes plane waves with Hadamard matrices and can increase SNR without sacrificing temporal or spatial resolution. Velocity vectors are then estimated using a weighted least squares (WLS) approach, where iterative residual-based weight optimization enhances robustness to noise and reduces contributions of outliers. The effectiveness of this technique is validated through simulations using a spiral blood flow phantom, indicating a substantial improvement in velocity estimation accuracy, especially in deep imaging regions with significant signal attenuation. In vivo experiments on rat brains further corroborate that the proposed method has higher accuracy than existing UVD approaches, especially for small vessels. Notably, our method can accurately distinguish between arterial flow and venous flow by analyzing pulsatility and resistivity within the cerebral vascular network.</sec><sec>This work demonstrates the potential of PC-UVD in complex vascular imaging, providing high SNR, high temporal and spatial resolution, and accurate vectorized flow measurements. Our results highlight its ability to non-invasively evaluate hemodynamic parameters and its potential application in the diagnosis of cerebrovascular diseases, particularly in small vessels.</sec>