Improved Absolute Quantification using Bayesian Penalized Likelihood Reconstruction on a Digital PET/CT – Towards True Uptake Measurement
Year: 2020
Type: preprint
Abstract: Background To evaluate the quantification accuracy, we measured the recovery coefficient (RC) and contrast recovery (CR) in a phantom study and used it as a guide in clinical evaluation about digital positron emission tomography -computed tomography (PET/CT). Methods The RC and CR of the PET images reconstructed with 4 different methods [ordered subsets expectation maximazation (OSEM), time of flight(TOF), TOF-point spread function (PSF), bayesian penalized likelihood (BPL)] were measured in the phantom study. And, SUVmax and SUVmean (the maximum and mean of the standardized uptake values, SUVs) of 75 small pulmonary nodules (sub-centimeter group: <10 mm and medium size group: 10 - 25 mm) from 26 patients were measured with those methods. For lesions smaller than 3 times of the spatial resolution, partial-volume-effect correction (PVC) based on RC values was performed. Results BPL enables higher RC and CR values, which can improve quantification accuracy compared with other methods (P < 0.05), except for the RC of 37 mm sphere (P > 0.05). The SUVs of sub-centimeter fludeoxyglucose (FDG)-avid pulmonary nodules with BPL illustrated highly significant difference from those reconstructed with other algorithms (P<0.001). However, reconstruction algorithms had less impact on SUVs measured in medium group (P>0.05). With the PVC, highly significant difference (P<0.001) still existed in the SUVmean measured on BPL compared with those measured on the other 3 algorithms, although PVC had the least effect on BPL. Conclusions BPL on the digital PET/CT demonstrated improved quantification accuracy. For small pulmonary nodules, PVC is important.
Authors: Zhifang Wu, Zhixing Qin, Bin Huang, Binwei Guo, Xinzhong Hao, Ping Wu, Bin Zhao, Jun Xie, Sijin Li
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4243271538