## 4 Discussion
The encoder-decoder architecture used for the generator of the proposed network contributed to the generation of the out-painted images, harmonizing with the input images, and the critical features of the input images for out-painting were extracted and provided during network training by the dilated convolution block. Also, the dual-discriminator ensured the preservation of original textures for the non-truncated regions as well as the authenticity of generated images for the truncated regions. The generator of the proposed network was able to selectively learn the image generation task for the truncated regions by differentiating the truncated regions from the non-truncated regions using the binary mask images. These strategies comprehensively affected the performance of the proposed out-painting method. With an increase in the number of focal spots, the SSIMs of the proposed out-painting method were slightly lower than those of the PDC method. This reduction was attributed to a loss of high-resolution information during network training, as can be seen in the out-painted regions of the differential images. Generally, the convolution layers in the GAN generator inherently downsample the features of an input image, and this process interrupts the effective transmission of fine-grained details to deeper layers [28,29]. Furthermore, the pixel-wise loss functions utilized in this study are limited to fully preserving the high-frequency components in an output image because the loss functions focus on reporting explicit pixel-level constraints rather than analyzing global similarities between input and output images [30]. Thus, the incomplete restoration of structural details induced a decrease in the SSIMs for the s-IGDT images acquired by the proposed out-painting method, and this phenomenon was more emphasized for the s-IGDT images with a large number of focal spots because the effect was accumulated by a reconstruction process. Several studies have reported that GAN-based networks, including residual blocks and skip connections, are able to improve the spatial resolution of output images [31,32]. And, the perceptual loss is utilized for restoring structural details in image transformation and enhancement tasks [33,34]. Accordingly, the further refinements of the network architecture with the residual block and skip connection are necessary in order to overcome the issue of spatial resolution degradation, and the perceptual loss function can be used to prevent the loss of high-level features. Although the promising results demonstrated the performance of the proposed out-painting method in mitigating the truncation artifacts of the s-IGDT images, this study has several issues that should be addressed in future studies. We simulated the s-IGDT projections using the digital phantoms, which were constructed by using the clinical CT images acquired with the various tube voltages, tube currents, and slice thicknesses for different patients. And, a substantial portion of the obtained projections was allocated to the training images in order to enhance the trained model's performance under the given dataset. This strategy led to a lack of test images, and consequently, the test images were prepared by using a single phantom. Thus, future studies should incorporate a broader and more diverse set of test images for generalizing the proposed method, and the cross-validation between the training and test images is necessary to ensure the robustness and statistical reliability of the performance evaluation. Several s-IGDT systems have been pre-clinically tested for practical applications, and the various scan strategies have been reported for optimizing their applications [4,5,35,36]. Thus, the proposed out-paintingmethod needs to be implemented in accordance with the applications, and its performance should be experimentally investigated for enhancing the clinical applicability. Also, the effects of the residual block, skip connection, and perceptual loss function in the out-painting task would be evaluated in the near future.