## 3 Results  
To evaluate the trained model, a test dataset containing 800 pairs of motion-blurred CT images and their corresponding 60 motion images was used. It was generated from eight different motion videos that were excluded from the training dataset. Two examples of the performance of the trained model are shown in Figure 5. We only selected several images as the representative of each phase to qualitatively analyze the performance of our model. Overlapped images were used to demonstrate the structural similarity between the prediction and ground truth. They were produced by overlapping color images that were converted from the ground truth and predicted images. In the overlapped images, correctly predicted pixels will turn yellow or remain black, whereas mispredicted pixels will remain green and red. A typical example with good performance is shown in Figure 5a. There are few green and red pixels in the overlapped images (Figure 5a), indicating that the structures of predicted images are close to the ground truth images. A typical example with bad performance is shown in Figure 5b. Predicted images in the closed phase and predicted images in the slow closing phase are close to the ground truth images (Figure 5b). However, predicted images during the rapid opening phase differ from the ground truth images. There are significant branches with green and red pixels in the overlapped images of these images. To further learn the distribution of correct and failed predictions, 50 pairs of motion-blurred CT images and their corresponding 60 motion images were randomly selected from the test dataset. Then, we checked overlapped images. Correctly predicted motion images were treated as correct predictions, while mispredicted motion images were treated as failed predictions (Table 5). A considerable performance drop can be seen in the rapid opening phase.  
To evaluate the trained model quantitatively, we used two image similarity evaluation metrics and three motion features evaluation metrics. The details are presented below.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Probability**} & **Images in the Closed** & **Images in the Rapid** & **Images in the Slow** \\  & **Phase** & **Opening Phase (N =** & **Closing Phase** \\  & **(N = 989)** & **1000)** & **(N = 1011)** \\ \hline Correct prediction & 92.3\% & 68.6\% & 83.3\% \\ Failed prediction & 7.7\% & 31.4\% & 16.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The distribution of correct predictions and failed predictions.  
Figure 5: _Cont_.  
Figure 5: Two examples of prediction results of our trained model. The image on the left is a motion-blurred CT image. We selected representative images from each motion phase. Images in the first row are ground truth and color-converted images. Images in the second row are predicted images and color-converted images. Images in the third row are partially enlarged images and overlapped images. In the overlapped images, correctly predicted pixels will turn yellow or remain black, whereas mispredicted pixels will remain red or green. (**a**) An example of good performance of our trained model. (**b**) An example of bad performance of our trained model. Our trained model failed in the images of rapid opening phase.