Blood flow evaluation in high-frequency, 40 MHz imaging: a comparative study of four vector velocity estimation...

Ultrasonic imaging is often used to estimate blood flow velocity. Currently, estimates are carried out using Doppler-based techniques. However, there are a number of shortcomings such as the limited spatial resolution and the inability to estimate longitudinal flows. Thus, alternative methods have been proposed to overcome them. Difficulties are notably encountered with high-frequency imaging systems that use swept-scan techniques. In this article, we propose to compare four vector velocity estimation methods that are complementary to Doppler, focusing on 40 MHz, high-frequency imaging. The goal of this study is to evaluate which method could circumvent the limitations of Doppler methods for evaluation of microcirculation, in the vessels having diameter on the order of 1 mm. We used two region-based approaches, one decorrelation-based approach and one spatiotemporal approach. Each method has been applied to seven flow sequences with various orientations and mean velocities. Four sequences were simulated with a system approach based on a 3D set of moving scatterers. Three experimental sequences were carried out by injecting blood-mimicking fluid within a gelatin phantom and then acquiring images with Visualsonics, Vevo 660 system. From velocity estimates, several performance criteria such as the normalized mean error or the normalized mean standard deviation were defined to compare the performance of the four estimators. The results show that region-based methods are the most accurate exhibiting mean errors less than 10% and mean standard deviation less than 13%. However, region-based approaches are those that require the highest calculative cost compared to the decorrelation-based method, which is the fastest. Finally, the spatiotemporal approach appeared to be a trade-off in terms of computational complexity and accuracy of estimates. It provides estimates with errors less than 10% for mean velocity and the CPU time is approximately 17s for a ROI of size 40*80 pixels.