Impact of γ factor in the penalty function of Bayesian penalized likelihood reconstruction (Q.Clear) to achieve high-resolution PET images

EJNMMI Physics

Original Research

Noriaki Miyaji¹  Tensho Yamao²  Kenta Miwa³  Yu Yakushiji⁴  Shohei Fukuda⁴  Shogo Nezu⁵  Tokiya Yoshii⁶  Rinya Kobayashi⁷  Kenji Ishii⁸  Yuto Kamitaka⁸  Kei Wagatsuma⁹ 

[1] Department of Nuclear Medicine, Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, 135-8550, Tokyo, Japan;Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6 Sakaemachi, 960-8516, Fukushima-shi, Fukushima, Japan;Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6 Sakaemachi, 960-8516, Fukushima-shi, Fukushima, Japan;Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-cho, Itabashi-ku, 173-0015, Tokyo, Japan;Department of Radiology, Fukushima Medical University Hospital, 1 Hikarigaoka, 960-1295, Fukushima, Fukushima, Japan;Department of Radiological Sciences, School of Health Sciences, International University of Health and Welfare, 2600-1 Kitakanemaru, 324-8501, Ohtawara, Tochigi, Japan;Department of Radiology, Ehime University Hospital, 454 Shitsukawa, 791-0204, Touon-shi, Ehime, Japan;Department of Radiology, Fukushima Medical University Hospital, 1 Hikarigaoka, 960-1295, Fukushima, Fukushima, Japan;Department of Radiology, Tokai University Hospital, 143 Shimokasuya, 259-1193, Isehara-shi, Kanagawa, Japan;Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-cho, Itabashi-ku, 173-0015, Tokyo, Japan;Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-cho, Itabashi-ku, 173-0015, Tokyo, Japan;School of Allied Health Sciences, Kitasato University, 1-15-1 Kitazato, Minami-ku, 252-0373, Sagamihara, Kanagawa, Japan;

关键词: β;    BPL;    BSREM;    γ;    Q.Clear;    Sub-centimeter lesion;   

DOI  :  10.1186/s40658-023-00527-w

 received in 2022-01-23, accepted in 2023-01-16,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe Bayesian penalized likelihood PET reconstruction (BPL) algorithm, Q.Clear (GE Healthcare), has recently been clinically applied to clinical image reconstruction. The BPL includes a relative difference penalty (RDP) as a penalty function. The β value that controls the behavior of RDP determines the global strength of noise suppression, whereas the γ factor in RDP controls the degree of edge preservation. The present study aimed to assess the effects of various γ factors in RDP on the ability to detect sub-centimeter lesions.MethodsAll PET data were acquired for 10 min using a Discovery MI PET/CT system (GE Healthcare). We used a NEMA IEC body phantom containing spheres with inner diameters of 10, 13, 17, 22, 28 and 37 mm and 4.0, 5.0, 6.2, 7.9, 10 and 13 mm. The target-to-background ratio of the phantom was 4:1, and the background activity concentration was 5.3 kBq/mL. We also evaluated cold spheres containing only non-radioactive water with the same background activity concentration. All images were reconstructed using BPL + time of flight (TOF). The ranges of β values and γ factors in BPL were 50–600 and 2–20, respectively. We reconstructed PET images using the Duetto toolbox for MATLAB software. We calculated the % hot contrast recovery coefficient (CRChot) of each hot sphere, the cold CRC (CRCcold) of each cold sphere, the background variability (BV) and residual lung error (LE). We measured the full width at half maximum (FWHM) of the micro hollow hot spheres ≤ 13 mm to assess spatial resolution on the reconstructed PET images.ResultsThe CRChot and CRCcold for different β values and γ factors depended on the size of the small spheres. The CRChot, CRCcold and BV increased along with the γ factor. A 6.2-mm hot sphere was obvious in BPL as lower β values and higher γ factors, whereas γ factors ≥ 10 resulted in images with increased background noise. The FWHM became smaller when the γ factor increased.ConclusionHigh and low γ factors, respectively, preserved the edges of reconstructed PET images and promoted image smoothing. The BPL with a γ factor above the default value in Q.Clear (γ factor = 2) generated high-resolution PET images, although image noise slightly diverged. Optimizing the β value and the γ factor in BPL enabled the detection of lesions ≤ 6.2 mm.

【 授权许可】

CC BY   
© The Author(s) 2023

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