## 4 Discussion
### Interpretation of Motion Feature Evaluation Metrics  
To evaluate the ability of the trained model to predict motion features, we must know that the average maximum opening distance of the test dataset is 12.4 mm, the average maximum-swept area velocity of the test dataset is approximately 1888.5 mm\({}^{2}\)/s, and the time for the CT simulator to generate one CT image is 183.3 ms. Based on the results in \begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} **Swept Area Velocity** \\ **(mm\({}^{2}\)/s)** \\ \end{tabular} & **SSIM** \\ \hline
0–200 & 0.974 & 1200–1400 & 0.965 \\
200–400 & 0.972 & 1400–1600 & 0.963 \\
400–600 & 0.971 & 1600–1800 & 0.962 \\
600–800 & 0.970 & 1800–2000 & 0.962 \\
800–1000 & 0.968 & 2000–2200 & 0.962 \\
1000–1200 & 0.966 & 2200–2400 & 0.962 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The swept area velocity and SSIM. Figure 7: An example of the relationship between velocity and accuracy. **(a)** The opening distance profile of endpoints. **(b)** The swept area velocity profile. **(c)** The SSIM profile. Table 7, the MDE is approximately 5.7% of the average max opening distance, the MVE is approximately 20.8% of the average maximum-swept area velocity, and the OTE is approximately 3.0% of the time for the CT simulator to generate one CT image. From the results, it was found that the trained DNN can infer opening time with a high accuracy. The high accuracy in inferring opening time confirmed that motion blur in CT images had temporal information of motion. Furthermore, we found that the trained DNN had some ability in inferring opening distance and maximum swept area velocity. The reasons for poor accuracy in terms of maximum swept area velocity is because of the relationship between velocity and accuracy and the velocity limitation of the trained DNN. Although there are still errors, considering the elusive motion information in the motion blur and the difficulty of dynamic imaging, these results are acceptable and a significant improvement over the results of traditional methods, which only extracted one sharp image from a motion-blurred CT image. In addition, we found that the trained model has some ability to distinguish different motions by analyzing the data distribution of maximum opening distance and maximum-swept area velocity. The average maximum opening distance and maximum-swept area velocity of prediction were close to the ground truth (Table 10). We can tell the difference between these eight motions based on their mean values. However, a large deviation in the data distribution is shown in Figure 6a,b. These data shows that the trained model required the mean value of multiple samples to distinguish different motions. Our trained model had some ability to acquire accurate maximum opening distance, maximum-swept area velocity, and even the swept area velocity profile. Since the motions were varied by changing the geometric parameters and material properties, we further explored the relationship between these parameters and motion features. Geometric parameters affect the turbulence around aortic valves, whereas the material properties affect the bending stiffness and ductility of aortic valves. The geometric parameters can be easily measured from CT images of aortic valves in the closed phase, but it is difficult to evaluate the material properties of aortic valves. However, the detailed motion features obtained based on the proposed method can provide a new possibility for the evaluation of the material properties of aortic valves, inferring material properties from detailed motion features. There is a definite physical relationship between motion and material properties. Inferring material properties from detailed motion features can be considered an ill-posed problem with solutions, such as deep learning. Similar studies have been performed in other fields. [PERSON], et al. [38] proposed a method to infer material properties from small motion in video and [PERSON], et al.