Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction

Lei Xu; Ru-Shuai Li; Run-Ze Wu; Rui Yang; Qin-Qin You; Xiao-Chen Yao; Hui-Fang Xie; Yang Lv; Yun Dong; Feng Wang; Qing-Le Meng

doi:10.1186/s40658-022-00451-5

Small lesion depiction and quantification accuracy of oncological ¹⁸F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction

EJNMMI Phys. 2022 Mar 26;9(1):23. doi: 10.1186/s40658-022-00451-5.

Authors

Lei Xu^#¹, Ru-Shuai Li^#¹, Run-Ze Wu², Rui Yang¹, Qin-Qin You¹, Xiao-Chen Yao¹, Hui-Fang Xie³, Yang Lv³, Yun Dong³, Feng Wang⁴, Qing-Le Meng⁵

Affiliations

¹ Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, 210006, China.
² United Imaging Healthcare, 2258 Chengbei Road, Shanghai, 201870, China. qingle.meng@163.com.
³ United Imaging Healthcare, 2258 Chengbei Road, Shanghai, 201870, China.
⁴ Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, 210006, China. fengwangcn@hotmail.com.
⁵ Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, 210006, China. Runze.wu@gmail.com.

^# Contributed equally.

Abstract

Background: To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with ¹⁸F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification.

Methods: The CTN phantom was filled with ¹⁸F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with ¹⁸F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 mm³. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The SUV_max, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale.

Results: In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the SUV_max, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference.

Conclusions: The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8-0.9 was optimal for SVB reconstruction for the small tumor detection with ¹⁸F-FDG PET/CT.

Keywords: Bayesian penalized likelihood reconstruction; FDG; Lung nodule; PET; Small lesion detection; Small voxel reconstruction.

Abstract

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