Abstract

Purpose

Efforts have been made both to avoid invasive blood sampling and to shorten the scan duration for dynamic positron emission tomography (PET) imaging. A total-body scanner, such as the uEXPLORER PET/CT, can relieve these challenges through the following features: First, the whole-body coverage allows for noninvasive input function from the aortic arteries; second, with a dramatic increase in sensitivity, image quality can still be maintained at a high level even with a shorter scan duration than usual. We implemented a dual-time-window (DTW) protocol for a dynamic total-body 18F-FDG PET scan to obtain multiple kinetic parameters. The DTW protocol was then compared to several other simplified quantification methods for total-body FDG imaging that were proposed for conventional setup.

Methods

The research included 28 patient scans performed on an uEXPLORER PET/CT. By discarding the corresponding data in the middle of the existing full 60-min dynamic scan, the DTW protocol was simulated. Nonlinear fitting was used to estimate the missing data in the interval. The full input function was obtained from 15 subjects using a hybrid approach with a population-based image-derived input function. Quantification was carried out in three areas: the cerebral cortex, muscle, and tumor lesion. Micro- and macro-kinetic parameters for different scan durations were estimated by assuming an irreversible two-tissue compartment model. The visual performance of parametric images and region of interest-based quantification in several parameters were evaluated. Furthermore, simplified quantification methods (DTW, Patlak, fractional uptake ratio [FUR], and standardized uptake value [SUV]) were compared for similarity to the reference net influx rate Ki.

Results

Ki and K1 derived from the DTW protocol showed overall good consistency (P < 0.01) with the reference from the 60-min dynamic scan with 10-min early scan and 5-min late scan (Ki correlation: 0.971, 0.990, and 0.990; K1 correlation: 0.820, 0.940, and 0.975 in the cerebral cortex, muscle, and tumor lesion, respectively). Similar correlationss were found for other micro-parameters. The DTW protocol had the lowest bias relative to standard Ki than any of the quantification methods, followed by FUR and Patlak. SUV had the weakest correlation with Ki. The whole-body Ki and K1 images generated by the DTW protocol were consistent with the reference parametric images.

Conclusions

Using the DTW protocol, the dynamic total-body FDG scan time can be reduced to 15 min while obtaining accurate Ki and K1 quantification and acceptable visual performance in parametric images. However, the trade-off between quantification accuracy and protocol implementation feasibility must be considered in practice. We recommend that the DTW protocol be used when the clinical task requires reliable visual assessment or quantifying multiple micro-parameters; FUR with a hybrid input function may be a more feasible approach to quantifying regional metabolic rate with a known lesion position or organs of interest.

Details

Title
Comparison between a dual-time-window protocol and other simplified protocols for dynamic total-body 18F-FDG PET imaging
Author
Wang, Zhenguo 1 ; Wu, Yaping 2 ; Li, Xiaochen 2 ; Bai, Yan 2 ; Chen, Hongzhao 1 ; Ding, Jie 1 ; Shen, Chushu 1 ; Hu, Zhanli 1 ; Liang, Dong 1 ; Liu, Xin 1 ; Zheng, Hairong 1 ; Yang, Yongfeng 1 ; Zhou, Yun 3 ; Wang, Meiyun 2 ; Sun, Tao 4   VIAFID ORCID Logo 

 Chinese Academy of Sciences, Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Shenzhen, People’s Republic of China (GRID:grid.9227.e) (ISNI:0000000119573309) 
 University of Zhengzhou, Henan Provincial People’s Hospital and the People’s Hospital of Zhengzhou, Zhengzhou, People’s Republic of China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846) 
 United Imaging Healthcare Group Co., Ltd, Central Research Institute, Shanghai, People’s Republic of China (GRID:grid.497849.f); Shanghai Tech University, School of Biomedical Engineering, Shanghai, People’s Republic of China (GRID:grid.440637.2) (ISNI:0000 0004 4657 8879) 
 Chinese Academy of Sciences, Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Shenzhen, People’s Republic of China (GRID:grid.9227.e) (ISNI:0000000119573309); United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, People’s Republic of China (GRID:grid.9227.e) 
Publication year
2022
Publication date
Dec 2022
Publisher
Springer Nature B.V.
e-ISSN
21977364
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2714220204
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.