Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain

Abstract

High-quality and fast reconstructions are essential for the clinical application of positron emission tomography (PET) imaging. Herein, a deep-learning-based framework is proposed for PET image reconstruction directly from the sinogram domain to achieve high-quality and high-speed reconstruction at the same time. In this framework, conditional generative adversarial networks are constructed to learn a mapping from sinogram data to a reconstructed image and to generate a well-trained model. The network consists of a generator that utilizes the U-net structure and a whole-image strategy discriminator, which are alternately trained. Simulation experiments are conducted to validate the performance of the algorithm in terms of reconstruction accuracy, reconstruction efficiency, and robustness. Real patient data and Sprague Dawley rat data were used to verify the performance of the proposed method under complex conditions. The experimental results demonstrate the superior performance of the proposed method in terms of image quality, reconstruction speed, and robustness.