Related Experiment Video
Updated: Sep 21, 2025
![Semi-quantitative Assessment Using [18F]FDG Tracer in Patients with Severe Brain Injury](/_next/image?url=https%3A%2F%2Fcloudfront.jove.com%2FCDNSource%2Fteasers%2F58641.jpg&w=3840&q=50)
Semi-quantitative Assessment Using [18F]FDG Tracer in Patients with Severe Brain Injury
Published on: November 9, 2018
Feature-based Quality Assessment of Middle Cerebral Artery Occlusion Using 18F-Fluorodeoxyglucose Positron Emission
Wuxian He1, Hongtu Tang2, Jia Li2
1Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
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.
Insights
Assessing the quality of middle cerebral artery occlusion (MCAO) in animal stroke models is vital. This study introduces a novel, automated FDG PET image analysis protocol for accurate MCAO quality assessment.
Area of Science:
- Neuroscience
- Medical Imaging
- Biomedical Engineering
Background:
- Accurate quality assessment of the middle cerebral artery occlusion (MCAO) model in animal studies is critical for reliable ischemic stroke research.
- Current methods for assessing MCAO quality using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) are limited by inconsistent preprocessing, biased normalization, and unclear spatiotemporal uptake analysis.
Purpose of the Study:
- To develop and validate a novel, automated image feature-based protocol for assessing the quality of MCAO procedures in FDG PET studies.
- To provide a quantitative, objective, and reliable tool that overcomes the limitations of existing MCAO quality assessment methods.
Main Methods:
- A 3D scale-invariant feature transform (SIFT) combined with a support vector machine (SVM) was employed to analyze image features.
- The proposed protocol was designed to be fully quantitative, objective, and automatic, bypassing the need for intensity normalization.
Main Results:
- The developed feature-based protocol offers a convenient and accurate method for assessing MCAO procedure quality in FDG PET imaging.
- The protocol demonstrates objectivity and automation, eliminating the dependency on intensity normalization steps common in other approaches.
- An online interface was created to facilitate image checking and assessment result retrieval.
Conclusions:
- The proposed image feature-based protocol provides a significant advancement in the quality assessment of MCAO procedures in FDG PET studies.
- This automated and quantitative approach enhances the reliability and reproducibility of animal models for ischemic stroke research.
- The developed tool and online interface offer a practical solution for researchers in the field.
