## 3 Results and Discussions
### Validation of CORDIC Algorithm  
After signal processing, the ultrasonic data of the radial artery were stored in the RAM. The upper computer reads the ultrasonic data from the hardware via the USB driver, forming a frame of ultrasonic data. The image of the radial artery of the wrist of a healthy adult male displayed on the upper computer without signal processing is shown in Figure 11. As shown in the figure, the background is relatively bright, the noise is relatively high, and the contrast of the radial artery contours is not very clear. It does not achieve the effect of distinguishing different media, filtering out the noise, amplifying small signals, and suppressing large signals. Additionally, the image of the radial artery in the wrist of the same healthy adult male displayed on the host computer after ultrasound signal processing is shown in Figure 12. Compared to Figure 11, the result shows a wealth of clear blood flow information, with weak echoes from other stationary tissues, such as the vessel wall, and a relatively obvious delineation between blood and fixed tissue without artifacts. In summary, after the signal processing module based on the high-performance CORDIC algorithm, the image has high brightness and good imaging quality, which helps to observe blood flow in real-time and assist in disease diagnosis, basically meeting the requirements of ultrasound imaging work. Although the upper edge of the blood vessel wall is not very clear, it has little effect on the observation and diagnosis of blood vessel images. Image processing techniques can be studied to further improve image quality. The difference in vessel diameters obtained by scanning the probe parallel and perpendicular to the radial artery vessels is approximately 0.03 mm. In this way, it can indirectly verify the effectiveness of the high-performance CORDIC algorithm used in radial artery ultrasound signal processing in this paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resource** & **Utilization** & **Available** & **Utilization \%** \\ \hline Logic elements & 1232 & 15,408 & 8\% \\ Register & 924 & 15,408 & 6\% \\ Embedded memory & 15,482 & 516,096 & 3\% \\ Embedded multipliers & 2 & 56 & 3\% \\ Pins & 64 & 166 & 39\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The hardware resource utilization of ultrasonic signal processing technology is based on the high–performance CORDIC algorithm. To ensure the repeatability and reliability of the experiment, twenty volunteers, including ten males and ten females, were selected to participate in this experiment. The basic physiological data of the subjects are shown in Table 5. After each subject completed one radial artery data acquisition, the left wrist was removed, and the next acquisition was performed, with each acquisition held in a consistent position. The radial artery data were collected six times for each subject, with the probe taken three times parallel and three times perpendicular to the radial artery at the positions of three reference points, respectively [52]. Finally, the average values of the measured data were calculated as the final diameter of the radial artery. The comparison of radial artery diameters in subjects with different probe scanning modalities is shown in Figure 13. Note that the radial artery diameters measured in the parallel and perpendicular vessel directions of the probe are almost identical, with the radial artery diameters ranging from 2.51 to 3.02 mm in males and 2.20 to 2.63 mm in females, which is consistent with the actual radial artery diameters in humans [53]. The result also verifies the effectiveness of applying ultrasound signal processing technology based on the high-performance CORDIC algorithm for radial artery ultrasound imaging. Figure 11: Ultrasound image of the radial artery in the wrist of a healthy adult male without signal processing. (**a**) Probe scanning direction parallel to the vessel; (**b**) Probe scanning direction perpendicular to the vessel.