Low-count PET image reconstruction based on truncated inverse radon layer and U-shaped network

Abstract

Objective: Positron emission tomography (PET) is a functional imaging widely used in various applications such as tumour detection. PET image reconstruction is an ill-posed inverse problem, and the model-based iterative reconstruction methods commonly used in clinical practice have disadvantages such as long time consumption and low signal-to-noise ratio, especially at low doses. Approach: In this study, we propose a deep learning-based reconstruction method that is capable of reconstructing images directly from low-count sinograms. Our network consists of two parts, a truncated inverse radon layer for implementing domain transform and a U-shaped network for image enhancement. Main result: We validated our method on both simulation data and real data. Compared to ordered subset expectation maximization (OSEM) with a post-Guassian filter, the structural similarity can be improved to 31.22±2.86 and the peak signal-to-noise ratio can be improved by 5 dB. Significance: The proposed method can directly convert low-count sinograms into PET images, while obtaining improved image quality and having less time consumption than iterative reconstruction algorithms and the state-of-the-art CNN.