# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 5 Discussion  
PET has become an indispensable tool in clinical trials in recent years. The quality of the reconstructed image is crucial for the development of PET. However, traditional reconstruction methods have many limitations, as discussed above. Therefore, deep learning was adopted in this study to avoid the problems encountered in traditional methods. In this study, an attempt was made to learn the correspondence between sinogram images and reconstruction images. Considering that cGANs have obtained outstanding results in other fields, a cGAN was chosen as the main network. The results of the simulation, SD rat, and human brain datasets suggest that cGANs can outperform traditional methods.  
For the simulation datasets, the reconstruction image quality is greatly improved compared with MLEM and TV, and the quantitative results also prove that. The bias values of cGANs for the first few frames are only 10% of that of MLEM method and about 30% of TV. The variance values are even less than 1% of the other methods. For the late frames which contain less noise and more photons, the bias and variance are much lower. For the reconstruction time, the deep-learning-based cGANs takes only 3% of the time of MLEM.  
However, although satisfactory results were obtained for the simulation dataset, the performance on the SD rat and real patient datasets was not satisfactory. There are two reasons for this. First, the two real datasets had a more complex data acquisition environment.  
Figure 14: Reconstruction results of five selected images of human brain dataset: 1–59 depicts the 59 th slice of the first test object, 2–34 depicts the 34 th slice of the second test object, etc.  
Figure 15: Reconstruction results of five selected images of human brain dataset.  
Moreover, there were significant differences between different individuals. Therefore, more data are required in the training data phase. Second, there was no additional information as a constraint in the network to guide the training process. Thus, it was difficult for the network to converge to more precise results.  
In future work, more physiological information should be added to the network as an effective constraint. The inputs of the network can be multiple-scale images, which can lead the network to learn coarse and fine structures.  
**Author Contributions:** Formal analysis, Z.L.; methodology, Z.L.; validation, Z.L.; writing--original draft Z.L. and H.L.; data curation, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No.: U1809204, 61525106), by the National Key Technology Research and Development Program of China (No.: 2017 YFE0104000), and by the Key Research and Development Program of Zhejiang Province (No.: 2021C03029).  
**Institutional Review Board Statement:** Animal experiments were approved by the experimental welfare and ethics review committee of Zhejiang University, and were performed in compliance with the guidelines for animal experiments and local legal requirements.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.