Affiliations:
School of Biomedical Engineering, Southern Medical University, Guangdong, Guangzhou, China
Correspondence:
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Corresponding author: Wufan Chen, School of Biomedical Engineering, Southern Medical University, Guangzhou,
Guangdong 510515, China. Tel.: +86 02061648281; E-mail: chenwf@smu.edu.cn, 82094896@qq.com.
Abstract: BACKGROUND: Dynamic positron emission tomography (PET) is a powerful tool that provides useful quantitative information on physiological and biochemical processes. However, the low signal-to-noise ratio (SNR) in short dynamic frames is a challenge. OBJECTIVE: To get high SNR in the dynamic PET and to achieve high-quality PET parametric image are the objective of this study. METHODS: Low-rank (LR) modeling and edge-preserving prior are incorporated in this study with a unified mathematical framework to improve the SNR of a dynamic PET image series. The proposed algorithm is designed to reduce noise in homogeneous areas while preserving the edges of regions of interest. RESULTS: The performance of the proposed method (LRH) is compared both visually and quantitatively by using the classic Gaussian filter and an LR expression filter on a digital brain phantom and in vivo rat study. Experimental results demonstrate that the proposed filter can achieve superior visual and quantitative performance without sacrificing spatial resolution. CONCLUSIONS: The proposed LRH is considerably effective and exhibits great potential in processing dynamic PET data with high noise levels.
