Feature-based Quality Assessment of Middle Cerebral Artery Occlusion Using 18F-fluorodeoxyglucose Positron Emission Tomography

BackgroundThe association between brain metabolic change and ischemic stroke has attracted a lot of attention in the research community. 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging is widely used to measure the metabolism. In experiments, ischemic stroke is usually induced through middle cerebral artery occlusion (MCAO), and quality assessment of this procedure is of vital importance. However, an assessment method based on FDG PET images is still lacking. Herein, we propose an image feature-based protocol to assess the quality of the procedure. MethodsWe performed permanent MCAO to a total of 161 Sprague-Dawley rats. FDG micro-PET images were acquired both before and after the MCAO procedure. Triphenyl tetrazolium chloride (TTC) staining was also conducted to obtain ground truth of the infarct volume. After preprocessing of the PET images, a combination of 3D scale invariant feature transform (SIFT) and support vector machine (SVM) was applied to extract features and train a classifier that can assess the quality of the MCAO procedure. Results106 rats and 212 images were used as training data to construct the classification model. The SVM classifier achieved over 98% accuracy in cross validation. 10 rats with TTC results showing infarction in the ipsilateral brain region served as validation data. Their images were tested by the classifier and all of them were categorized into the correct group. Finally, the remaining 45 rats from a separate experiment were treated as independent test data. The prediction accuracy for these 90 images reached the level of 91%. An online interface was constructed for users to upload their images and obtain the assessment results. ConclusionThis feature-based protocol provides a convenient, accurate and reliable tool to assess the quality of the MCAO procedure in FDG PET study.