## 2 Methods
### Data Generation Process
#### 2.1.1 Motion Simulator  
To generate a reasonable movement of aortic valves, a 2D motion simulator was designed on ANSYS Workbench platform based on 2-way FSI theory. ANSYS FLUENT and ANSYS MECHANICAL APDL were used to solve the fluid and structure fields, respectively. These two fields were coupled using the system coupling module. Although the motion simulator cannot perfectly simulate the real physical process, it can reproduce the complex motion variation depending on FSI. The geometries of the numerical model were developed using the geometry study of aortic valves [25; 26]. The geometry of aortic valves is shown in Figure 2b, and the geometric parameters are shown in Table 1. These geometric parameters were sampled according to the normal distribution with the known mean and standard deviation of each parameter in Table 1.  
Figure 1: Overview of the proposed method. (**a**) Data generation process. First, various simulation parameters were input into the motion simulator to generate different motion videos. The videos were then converted to video stream data in the Vid2 im module. The amount of video stream data was increased following data augmentation. Finally, motion-blurred CT images were generated by the CT simulator using the augmented video stream data, and motion images were evenly taken from the video stream data used in CT imaging. (**b**) DNN training process. The DNN was trained using a dataset that included motion-blurred CT images and their corresponding motion images. Figure 2: Data generation process. (**a**) A sample of the data. (**b**) Geometry of aortic valves. (**c**) Velocity profile of inlet in the two-way fluid structure interaction theory (2-way FSI). (**d**) Image in the video stream data. (**e**) Geometry of the equiangular fan-beam CT. (**f**) Relationship between the position of images and CT simulator. (**g**) Relative timing setting of CT measurement toward the aortic valve motion. The scan start moment was randomly selected to generate motion-blurred CT images. In the structure field, the material properties of aortic valves were defined. For simplicity, a linear elastic material model was used. The values of these parameters were selected using FSI studies in this area. The detailed parameters are shown in Table 2. These material parameters were sampled according to the random distribution, with the known range of each parameter, in Table 2.  
In the fluid field, the parameters of blood flow were defined. For simplicity, the blood flow was assumed to be incompressible Newtonian with constant blood density of 1060 kg/m\({}^{3}\), constant viscosity of 0.0035 l/s. For turbulence modeling, the k-\(\epsilon\) model was used [27]. The inlet was defined as the velocity inlet. Based on the measurement of Doppler [28], a patient specified inlet velocity was used (Figure 2c). The main causes of aortic valve diseases are changes in geometry and material properties [29]. By varying the geometric parameters and material properties, 80 motion videos with different velocities and deformations were generated. With the generated videos, we extracted image frame by frame from motion videos to generate video stream data in the Vid2 im module. The images in the video stream data are grayscale images with a size of 512 \(\times\) 512 and a spatial resolution of 0.5 mm/pixel. An image in the video stream data is shown in Figure 2d. Background tissue was painted in the video stream data in the Vid2 im module to make the data more realistic. The pixel values of each component are determined using the CT value (Table 3). The proportion of each component was used to determine the pixels of the contact area among the aortic valve, blood, and background tissue. The midpoint of the aortic valves was defined as the midpoint of the aortic annulus (Figure 2d).