# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 1 Introduction
An unsupervised deep learning framework for direct reconstruction with an MR image as prior information would later be proposed by [PERSON] [28]. GapFill-Recon Net [29] is also a domain transform reconstruction network based on CNN. Recently, the deep image prior (DIP) framework [30] showed that CNNs have the intrinsic ability to regularize various ill-posed inverse problems without pretraining. No prior training pairs are required, and random noise can be employed as the network input to generate denoised images. Inspired by this work, [PERSON] et al. [31] achieved PET reconstruction guided by MRI using the DIP framework. [PERSON] et al. [32] designed a CNN-based PET reconstruction framework with multichannel prior inputs, including high-resolution magnetic resonance images and the radial and axial coordinates of each voxel. However, as the DIP method does not require pretraining [31], it is similar to an online training process for each reconstruction. Moreover, several parameters must be trained, thus requiring a long time to complete the reconstruction each time. This drawback limits the application of the method in clinical practice. The difficulty in select the stopping point of the iterative training is another limitation. With the addition of prior knowledge of other modes, image registration has also become a technical difficulty. Compared with category A, category B makes full use of the information in the sinogram domain and implements direct reconstruction using deep learning. [PERSON] et al. [33] focused on direct reconstruction and adopted a deep encoder-decoder network to learn the mapping from the sinogram to the image. However, they mainly focused on the reconstruction speed rather than the image quality. Their proposed method was verified by simulation as opposed to real clinical data. In this study, a direct reconstruction deep learning scheme for the recovery of radioactivity maps from sinogram data was built using conditional generative adversarial networks (cGANs) [34], which obtained excellent results for the image-to-image translation task [35]. In the training phase, the input is an image pair that includes reconstructed images and sinogram images. After alternate training of the generator and discriminator, a well-trained model was established. In the testing phase, the input was only a sinogram image. After passing through the trained model, the reconstructed image was obtainedfrom the output. There are two contributions of this study. First, simulation experiments were used to verify the performance of the proposed method in terms of accuracy, robustness, and runtime. Second, considering that the testing data and training data of simulation experiments may have similar structures, the method was verified on a Sprague Dawley (SD) rat dataset that includes 12 different objects and a real patient dataset that contains nine objects. Both kinds of experimental results demonstrate the feasibility of the proposed method. Our preliminary cGANs-based direct network result has been published on MICCAI 2019 [36]. The remainder of this report is organized as follows. In Section 2, the proposed methodology is presented. The design of the simulation experiments and real data experiments of PET image reconstruction is described in Section 3, followed by the experimental results in Section 4. Finally, the results are discussed, and the paper is concluded in Section 5.