DPIR-Net: Direct PET Image Reconstruction Based on the Wasserstein Generative Adversarial Network

• Published in: IEEE transactions on radiation and plasma medical sciences Vol. 5; no. 1; pp. 35 - 43
• Main Authors: Hu, Zhanli, Xue, Hengzhi, Zhang, Qiyang, Gao, Juan, Zhang, Na, Zou, Sijuan, Teng, Yueyang, Liu, Xin, Yang, Yongfeng, Liang, Dong, Zhu, Xiaohua, Zheng, Hairong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomedical imaging; Comparative studies; Deep learning; Direct image reconstruction; Feature extraction; Generative adversarial networks; Image enhancement; Image processing; Image quality; Image reconstruction; Imaging techniques; Machine learning; Medical imaging; Neural networks; Noise reduction; Positron emission; Positron emission tomography; positron emission tomography (PET); Radiation; Radiation effects; Radioactive tracers; Regularization; small animal PET; Smoothness; Tomography; Wasserstein generative adversarial network (WGAN)
• DOI: 10.1109/TRPMS.2020.2995717
• ISSN: 2469-7311

Positron emission tomography (PET) is an advanced medical imaging technique widely used in various clinical applications, such as tumor detection and neurologic disorders. Reducing the radiotracer dose is desirable in PET imaging because it decreases the patient's radiation exposure. However, reducing the dose can also increase noise, affecting the image quality. Therefore, an advanced image reconstruction algorithm based on low-dose PET data is needed to improve the quality of the reconstructed image. In this article, we propose the use of a direct PET image reconstruction network (DPIR-Net) using an improved Wasserstein generative adversarial network (WGAN) framework to enhance image quality. This article provides two main findings. First, our network uses sinogram data as input and outputs high-quality PET images direct, resulting in shorter reconstruction times compared with traditional model-based reconstruction networks. Second, we combine perceptual loss, mean square error, and the Wasserstein distance as the loss function, which effectively solves the problem of excessive smoothness and loss of detailed information in traditional network image reconstruction. We performed a comparative study using maximum-likelihood expectation maximization (MLEM) with a post-Gaussian filter, a total variation (TV)-norm regularization, a nonlocal means (NLMs) denoising method, a neural network denoising method, a traditional deep learning PET reconstruction network, and our proposed DPIR-Net method and evaluated the proposed method using both human and mouse data. The mouse data were obtained from a small animal PET prototype system developed by our laboratory. The quantitative and qualitative results show that our proposed method outperformed the conventional methods.