Feature-based Quality Assessment of Middle Cerebral Artery Occlusion Using 18F-Fluorodeoxyglucose Positron Emission Tomography.
10.1007/s12264-022-00865-2
- Author:
Wuxian HE
1
;
Hongtu TANG
2
;
Jia LI
2
;
Chenze HOU
1
;
Xiaoyan SHEN
3
;
Chenrui LI
1
;
Huafeng LIU
4
;
Weichuan YU
5
Author Information
1. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
2. Department of Acupuncture and Moxibustion, Hubei University of Chinese Medicine, Wuhan, 430065, China.
3. College of Science, Zhejiang University of Technology, Hangzhou, 310023, China.
4. State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou , 310027, China. liuhf@zju.edu.cn.
5. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. eeyu@ust.hk.
- Publication Type:Journal Article
- Keywords:
Brain metabolism;
FDG PET imaging;
Ischemic stroke;
Middle cerebral artery occlusion;
SIFT classification
- MeSH:
Animals;
Fluorodeoxyglucose F18;
Infarction, Middle Cerebral Artery/diagnostic imaging*;
Positron-Emission Tomography/methods*
- From:
Neuroscience Bulletin
2022;38(9):1057-1068
- CountryChina
- Language:English
-
Abstract:
In animal experiments, ischemic stroke is usually induced through middle cerebral artery occlusion (MCAO), and quality assessment of this procedure is crucial. However, an accurate assessment method based on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is still lacking. The difficulty lies in the inconsistent preprocessing pipeline, biased intensity normalization, or unclear spatiotemporal uptake of FDG. Here, we propose an image feature-based protocol to assess the quality of the procedure using a 3D scale-invariant feature transform and support vector machine. This feature-based protocol provides a convenient, accurate, and reliable tool to assess the quality of the MCAO procedure in FDG PET studies. Compared with existing approaches, the proposed protocol is fully quantitative, objective, automatic, and bypasses the intensity normalization step. An online interface was constructed to check images and obtain assessment results.
