## 4 Discussion  
In this study, the proposed concept of inferring aortic valve motion images from a single motion-blurred CT image was demonstrated using in silico learning under certain conditions. After training with the simulated dataset, the performance of the trained model was evaluated using two image similarity evaluation metrics and three motion feature evaluation metrics. The results of two image similarity evaluation metrics (SSIM, PSNR) are shown in Table 6. By analyzing and comparing these results, we found that the trained model can learn motion images from motion blur. The accuracy of different phases was different; additionally, a correlation between velocity and accuracy was observed: the higher the velocity, the lower the accuracy. We further explored the velocity limitation of the trained model, which works under the condition of the swept area velocity below 1400 mm\({}^{2}\)/s. The results of three special evaluation metrics (MDE, MVE, and OTE) are shown in Table 7. After analyzing the results of these metrics, we found that the trained model can infer opening time with a high accuracy and also had some ability in predicting maximum opening distance and maximum-swept area velocity. Furthermore, these motion features may be used to roughly determine the material properties of aortic valves.  
Despite the velocity limitation, this study shows that the trained DNN has learned the motion images from a single motion-blurred CT image of aortic valves. The main difference between the proposed and traditional methods is the way to deal with motion blur. In the proposed method, the motion information hidden in the motion blur is learned via DNN to infer motion images, whereas traditional methods primarily use the spatial or transform domain distribution of the motion blur to suppress or remove motion blur.  
Figure 6: Results of motion feature evaluation. (**a**) Data distribution of maximum opening distance (mm). (**b**) Data distribution of maximum-swept area velocity (mm\({}^{2}\)/s). (**c**) Data distribution of opening time (ms). The black points are outliers that differ significantly from the ground truth, and they account for approximately 6.6% of the total.  
For example, a wavelet-based thresholding method [16] removed noise by finding some thresholds in the wavelet transform domain. In some motion compensation methods, additional information is used, but this information is only used to separate targets from motion blur, while the motion information in blur is not used. For example, an anatomy-guided registration algorithm [17] used anatomy structure to compensate for regions of interest and remove extra noise. This difference in method leads to differences in results. Our method can reconstruct the entire time series representing the valve motion from a single motion-blurred CT image. However, the conventional method recovers an image without temporal information from a single motion-blurred CT image. Motion images can provide far more information than a single image, such as motion features. Moreover, the amount of information in results between the proposed and traditional methods is different, indicating that the proposed method is considerably different from the traditional methods.