## 4 Discussion  
In this work, the DCGAN-based out-painting method was proposed for mitigating truncation artifacts in the s-IGDT images. The DCGAN architecture was modified by incorporating the dilated convolution block and dual-discriminator in order to enhance the accuracy of the out-painting task. The binary mask images were additionally employed as the input data of the generator for distinguishing between the truncated and non-truncated regions in a projection domain. The performance of the proposed out-painting method was compared to the PDC method in terms of visual observation, voxel value profiles, SNR, NRMSE, PSNR, and SSIM. The truncation effects are influenced by the geometric configuration and scan parameters of the s-IGDT system, and the network training of this study was performed considering these properties. Thus, in this study, the training and test projections were prepared without the data augmentation techniques because the geometric configuration and scan parameters can be distorted in the augmented images. The purpose of this study was to preliminarily verify the feasibility of the proposed method for mitigating the truncation artifacts in the s-IGDT images. Thus, the hyperparameters for network training were manually selected without optimal tuning. The results of visual assessment demonstrated that the proposed out-painting method was able to substantially mitigate truncation artifacts for the various s-IGDT system geometries, and its performance was superior to that of the PDC method. Also, the proposed out-painting method reduced the voxel value differences for the out-painted regions as well as preserved the image properties for the non-truncated regions. These findings represent that the proposed out-painting method can provide high-quality s-IGDT images without voxel value distortion, and the truncation artifact-free s-IGDT images can be acquired by using the proposed out-painting method regardless of system geometries. The SNRs of the s-IGDT images obtained by using the proposed out-painting method were 75.42-84.91 and 38.87-52.02% higher than those obtained with the truncated projections and PDC method, respectively. These enhancements describe that the proposed out-painting method is superior to the conventional s-IGDT imaging and PDC methods with respect to the noise characteristic of an output image and the pixel-wise similarity between the reference and output images, and this superiority is derived from the outstanding performance of the proposed method in mitigating the truncation artifacts. The proposed out-painting method achieved reductions in the NRMSEs ranging from 24.11 to 58.60 and 5.34-30.23% over the conventional s-IGDT imaging and PDC methods, respectively. The s-IGDT images obtained by using the proposed out-painting method had higher PSNRs than those obtained with the truncated projections and PDC method by 10.19-37.87 and 1.24-5.10%, respectively. Also, the SSIMs for the proposed out-painting method improved by 16.50-32.77% over those for the conventional s-IGDT imaging method. These results represent that the proposed  
Figure 13: (**a**) SNRs, (**b**) NRMSEs, (**c**) PSNRs, and (**d**) SSIMs of the s-IGDT images with the different numbers of focal spots, respectively. out-painting method is able to enhance the quantitative accuracy of the s-IGDT image compared to the conventional s-IGDT imaging method. Eventually, we can conclude that the strategies for implementing the proposed out-painting method led to a successive reduction in the truncation artifacts in the s-IGDT image. Also, the improvements in the noise property and quantitative accuracy indicate that the proposed out-painting method has a significant potential for clinical applications requiring precise image interpretation and guidance.