## 2 Approach
### Generator  
The structure of the generator of both the 2D cWGAN and 3D cWGAN used in this study is illustrated in Figure 2. The generator for the 2D cWGAN is a 2D U-Net that employs 2D convolution, as shown in Figure 2a. The input size of the model is 80 \(\times\) 80 \(\times\) 1 for the corrupted data used as input and conditioning data, and the model is designed to output data of the same size. The model consists of contraction and expansion stages in an autoencoder format. The contraction stage employs ReLU (rectified linear unit) activation functions and consists of 3 \(\times\) 3 convolutions and 4 max poolings. The expansion stage also uses ReLU as the activation function and is made up of 3 \(\times\) 3 convolutions and 2 \(\times\) 2 upsamplings. During the contraction stage, the input is downsampled, which reduces its spatial resolution and results in the loss of important details. Similarly, during the expansion stage, the upsampled feature maps must be merged with the feature maps from the contracting stage to recover the lost spatial information. To address these issues, skip connections are added. Detailed information about the model is provided in Table 1.  
Figure 1: Network architecture of 2D and 3D cWGAN. (**a**) 2D cWGAN, (**b**) 3D cWGAN. The 3D cWGAN generator uses a 3D U-Net model with 3D convolution, as shown in Figure 2b. The model is configured to take in input data of a size of 80 \(\times\) 80 \(\times\) 80 \(\times\) 1 and output results of the same size. It is also composed of contraction and expansion stages, similar to the 2D cWGAN, with the contraction stage using ReLU as the activation function, which is composed of 3 \(\times\) 3 \(\times\) 3 convolutions and 4 max pooling operations. The expansion stage also uses ReLU as the activation function, which is composed of 3 \(\times\) 3 \(\times\) 3 convolutions and 2 \(\times\) 2 \(\times\) 2 upsamplings. The accuracy of the prediction is improved by adding a skip connection, which is the same as with the 2D cWGAN. Detailed information on the model is shown in Table 2. In this study, the model prediction was performed using only the generator; this model is referred to as the 3D U-Net. Figure 2: Structure of 2D and 3D generator. (**a**) Generator of 2D cWGAN; (**b**) generator of 3D cWGAN. \begin{table}
\begin{tabular}{c c} \hline \hline
**Layer (Type)** & **Output Size** \\ \hline input\_1 (Input layer) & (80, 80, 1) \\ conv2d (Conv2D) & (80, 80, 64) \\ conv2d\_1 (Conv2D) & (80, 80, 64) \\ max\_pooling2d (MaxPooling2D) & (40, 40, 64) \\ conv2d\_2 (Conv2D) & (40, 40, 128) \\ conv2d\_3 (Conv2D) & (40, 40, 128) \\ max\_pooling2d\_1 (MaxPooling2D) & (20, 20, 128) \\ conv2d\_4 (Conv2D) & (20, 20, 256) \\ conv2d\_5 (Conv2D) & (20, 20, 256) \\ max\_pooling2d\_2 (MaxPooling2D) & (10, 10, 256) \\ conv2d\_6 (Conv2D) & (10, 10, 512) \\ conv2d\_7 (Conv2D) & (10, 10, 512) \\ max\_pooling2d\_3 (MaxPooling2D) & (5, 5, 512) \\ conv2d\_8 (Conv2D) & (5, 5, 1024) \\ conv2d\_9 (Conv2D) & (5, 5, 1024) \\ up\_sampling2d (Upsampling2D) & (10, 10, 1024) \\ conv2d\_10 (Conv2D) & (10, 10, 512) \\ concatenate (Concatenate) & (10, 10, 1024) \\ conv2d\_11 (Conv2D) & (10, 10, 512) \\ conv2d\_12 (Conv2D) & (10, 10, 512) \\ up\_sampling2d\_1 (Upsampling2D) & (20, 20, 512) \\ conv2d\_13 (Conv2D) & (20, 20, 256) \\ concatenate\_1 (Concatenate) & (20, 20, 512) \\ conv2d\_14 (Conv2D) & (20, 20, 256) \\ conv2d\_15 (Conv2D) & (20, 20, 256) \\ conv2d\_15 (Conv2D) & (20, 20, 256) \\ conv2d\_15 (Conv2D) & (20, 20, 256) \\ conv2d\_16 (Conv2D) & (40, 40, 128) \\ concatenate\_2 (Concatenate) & (40, 40, 256) \\ conv2d\_17 (Conv2D) & (40, 40, 128) \\ conv2d\_18 (Conv2D) & (40, 40, 128) \\ up\_sampling2d\_3 (Upsampling2D) & (80, 80, 128) \\ conv2d\_19 (Conv2D) & (80, 80, 64) \\ concatenate\_3 (Concatenate) & (80, 80, 128) \\ conv2d\_20 (Conv2D) & (80, 80, 64) \\ conv2d\_21 (Conv2D) & (80, 80, 64) \\ conv2d\_22 (Conv2D) & (80, 80, 2) \\ conv2d\_23 (Conv2D) & (80, 80, 1) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structure of 3D generator.