Positron emission tomography image reconstruction using feature extraction

Article type: Research Article

Authors: Gao, Juan^{a; 1} | Zhang, Qiyang^{a; b; 1} | Liu, Qiegen^c | Zhang, Xuezhu^d | Zhang, Mengxi^d | Yang, Yongfeng^a | Liang, Dong^a | Liu, Xin^a | Zheng, Hairong^a | Hu, Zhanli^{a; *}

Affiliations: [a] Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China | [b] Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China | [c] Department of Electronic Information Engineering, Nanchang University, Nanchang, China | [d] Department of Biomedical Engineering, University of California, Davis, CA, USA

Correspondence: [*] Corresponding author: Zhanli Hu, Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. E-mail: [email protected].

Note: [1] These authors contributed equally to this work.

Abstract: PURPOSE:To reduce the cost of positron emission tomography (PET) scanning systems, image reconstruction algorithms for low-sampled data have been extensively studied. However, the current method based on total variation (TV) minimization regularization nested in the maximum likelihood-expectation maximization (MLEM) algorithm cannot distinguish true structures from noise resulting losing some fine features in the images. Thus, this work aims to recover fine features lost in the MLEM-TV algorithm from low-sampled data. METHOD:A feature refinement (FR) approach previously developed for statistical interior computed tomography (CT) reconstruction is applied to PET imaging to recover fine features in this study. The proposed method starts with a constant initial image and the FR step is performed after each MLEM-TV iteration to extract the desired structural information lost during TV minimization. A feature descriptor is specifically designed to distinguish structure from noise and artifacts. A modified steepest descent method is adopted to minimize the objective function. After evaluating the impacts of different patch sizes on the outcome of the presented method, an optimal patch size of 7×7 is selected in this study to balance structure-detection ability and computational efficiency. RESULTS:Applying MLEM-TV-FR algorithm to the simulated brain PET imaging using an emission activity phantom, a standard Shepp-Logan phantom, and mouse results in the increased peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as comparing to using the conventional MLEM-TV algorithm, as well as the substantial reduction of the used sampling numbers, which improves the computational efficiency. CONCLUSIONS:The presented algorithm can achieve image quality superior to that of the MLEM and MLEM-TV approaches in terms of the preservation of fine structure and the suppression of undesired artifacts and noise, indicating its useful potential for low-sampled data in PET imaging.

Keywords: Positron emission tomography (PET), maximum likelihood-expectation maximization (MLEM), low-sampled data, total variation (TV), feature extraction

DOI: 10.3233/XST-190527

Journal: Journal of X-Ray Science and Technology, vol. 27, no. 5, pp. 949-963, 2019