An Automatic Method for Generating an Unbiased Intensity Normalizing Factor in Positron Emission Tomography Image Analysis After Stroke.
10.1007/s12264-018-0240-8
- Author:
Binbin NIE
1
;
Shengxiang LIANG
1
;
Xiaofeng JIANG
2
;
Shaofeng DUAN
1
;
Qi HUANG
1
;
Tianhao ZHANG
1
;
Panlong LI
1
;
Hua LIU
1
;
Baoci SHAN
3
Author Information
1. Division of Nuclear Technology and Applications, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China.
2. School of Public Health and Family Medicine, Capital Medical University, Beijing, 100069, China.
3. Division of Nuclear Technology and Applications, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China. shanbc@ihep.ac.cn.
- Publication Type:Journal Article
- Keywords:
FDG-PET imaging;
Intensity normalization;
Stroke;
Unbiased scale factor;
Voxel-wise analysis
- MeSH:
Animals;
Computer Simulation;
Disease Models, Animal;
Female;
Fluorodeoxyglucose F18;
Image Processing, Computer-Assisted;
methods;
Infarction, Middle Cerebral Artery;
diagnostic imaging;
Male;
Positron-Emission Tomography;
methods;
Rats;
Rats, Sprague-Dawley;
Stroke;
diagnostic imaging
- From:
Neuroscience Bulletin
2018;34(5):833-841
- CountryChina
- Language:English
-
Abstract:
Positron emission tomography (PET) imaging of functional metabolism has been widely used to investigate functional recovery and to evaluate therapeutic efficacy after stroke. The voxel intensity of a PET image is the most important indicator of cellular activity, but is affected by other factors such as the basal metabolic ratio of each subject. In order to locate dysfunctional regions accurately, intensity normalization by a scale factor is a prerequisite in the data analysis, for which the global mean value is most widely used. However, this is unsuitable for stroke studies. Alternatively, a specified scale factor calculated from a reference region is also used, comprising neither hyper- nor hypo-metabolic voxels. But there is no such recognized reference region for stroke studies. Therefore, we proposed a totally data-driven automatic method for unbiased scale factor generation. This factor was generated iteratively until the residual deviation of two adjacent scale factors was reduced by < 5%. Moreover, both simulated and real stroke data were used for evaluation, and these suggested that our proposed unbiased scale factor has better sensitivity and accuracy for stroke studies.
