Added value of PET Bayesian penalized likelihood reconstruction algorithm in the diagnosis of solitary pulmonary nodules/masses
10.3760/cma.j.cn321828-20220622-00195
- VernacularTitle:PET贝叶斯惩罚似然法对单发肺占位诊断的增益价值
- Author:
Mengchun LI
1
;
Meng LIANG
;
Jinfeng WANG
;
Jia WEN
;
Yiyi HU
;
Zhifang WU
Author Information
1. 山西医科大学第一医院核医学科，太原 030001
- Keywords:
Solitary pulmonary nodule;
Image processing, computer-assisted;
Bayes theorem;
Likelihood functions;
Positron-emission tomography
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2023;43(5):267-271
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the effects of silicon photomutipliers (SiPM) detector and Bayesian penalized likelihood (BPL) reconstruction algorithm on semiquantitative parameters of 18F-FDG PET/CT and diagnostic efficiency for solitary pulmonary nodules/masses compared with traditional photomultiplier tube (PMT) and ordered subsets expectation maximization (OSEM). Methods:From March 2020 to January 2022, 118 patients (76 males, 42 females, age (63.0±10.1) years) newly diagnosed with solitary pulmonary nodules/masses in First Hospital of Shanxi Medical University were prospectively enrolled and underwent 18F-FDG PET/CT imaging with two different PET/CT scanners successively. The images were divided into PMT+ OSEM, SiPM+ OSEM and SiPM+ BPL groups according to PET detector and reconstruction algorithms. The SUV max, SUV mean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of pulmonary nodules/masses were measured, then signal-to-noise ratio (SNR) and signal-to-background ratio (SBR) were calculated. One-way analysis of variance and Kruskal-Wallis rank sum test were performed to compare differences of above parameters among groups. ROC curve analysis was used to analyze the optimal threshold of SUV max for the differential diagnosis of pulmonary nodules/masses and AUCs were obtained. Results:There were 83 malignant nodules and 35 benign nodules. The image quality of SiPM+ BPL group (4.23±0.64) was better than that of SiPM+ OSEM group (3.57±0.50) or PMT+ OSEM group (3.58±0.51; F=54.85, P<0.001). There were significant differences in SUV max (7.57(3.86, 15.61) vs 4.95(2.22, 10.48)), SUV mean (4.43(2.28, 9.12) vs 2.84(1.21, 5.71)), MTV (3.54(1.57, 7.67) vs 5.09(2.83, 11.79)), SNR (28.12(12.55, 54.38) vs 20.16(8.29, 41.45)) and SBR (4.03(1.83, 7.75) vs 2.32(0.96, 5.03)) between SiPM+ BPL and SiPM+ OSEM groups ( H values: 16.63-37.05, all P<0.001). The optimal threshold values of SUV max in SiPM+ BPL, SiPM+ OSEM and PMT+ OSEM were 3.31, 2.21, 2.05 with AUCs of 0.686, 0.689, 0.615 for nodules < 2 cm, and were 10.29, 6.49, 4.33 with AUCs of 0.775, 0.782, 0.774 for nodules/masses ≥2 cm. Conclusions:Image quality and parameters of pulmonary nodules/masses are mainly affected by the reconstruction algorithms. BPL can improve SUV max, SUV mean, SBR and SNR, but reduce MTV without significant effect on liver parameters. SiPM+ BPL has a higher diagnostic threshold of SUV max than SiPM+ OSEM and PMT+ OSEM.
