Abstract

Statistical Parametric Mapping (SPM) is a computational approach for analysing functional brain images like Positron Emission Tomography (PET). When performing SPM analysis for different patient populations, brain PET template images representing population-specific brain morphometry and metabolism features are helpful. However, most currently available brain PET templates were constructed using the Caucasian data. To enrich the family of publicly available brain PET templates, we created Chinese-specific template images based on 116 [18F]-fluorodeoxyglucose ([18F]-FDG) PET images of normal participants. These images were warped into a common averaged space, in which the mean and standard deviation templates were both computed. We also developed the SPM analysis programmes to facilitate easy use of the templates. Our templates were validated through the SPM analysis of Alzheimer’s and Parkinson’s patient images. The resultant SPM t-maps accurately depicted the disease-related brain regions with abnormal [18F]-FDG uptake, proving the templates’ effectiveness in brain function impairment analysis.

Measurement(s)

brain metabolism measurement

Technology Type(s)

FDG-Positron Emission Tomography

Factor Type(s)

age • sex

Sample Characteristic - Organism

Homo sapiens

Sample Characteristic - Location

China

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16382418

Details

Title
Population-specific brain [18F]-FDG PET templates of Chinese subjects for statistical parametric mapping
Author
Wang, Hongkai 1 ; Yang, Tian 1 ; Liu, Yang 1 ; Chen, Zhaofeng 1 ; Zhai Haoyu 1 ; Zhuang Mingrui 1 ; Zhang, Nan 1 ; Jiang Yuanfang 2 ; Gao, Ya 2 ; Feng Hongbo 2 ; Zhang, Yanjun 2 

 Dalian University of Technology, School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian, China (GRID:grid.30055.33) (ISNI:0000 0000 9247 7930) 
 the First Affiliated Hospital of Dalian Medical University, Department of Nuclear Medicine, Dalian, China (GRID:grid.452435.1) (ISNI:0000 0004 1798 9070) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2602863829
Copyright
© The Author(s) 2021. 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.