Using the coefficient of determination to identify injury regions after stroke in pre-clinical FDG-PET images

• Published in: Computers in biology and medicine Vol. 184; p. 109401
• Main Authors: He, Wuxian, Tang, Hongtu, Li, Jia, Shen, Xiaoyan, Zhang, Xuechen, Li, Chenrui, Liu, Huafeng, Yu, Weichuan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2025; Elsevier Limited
• Subjects: Animals; Brain - diagnostic imaging; Brain injury; Cerebral blood flow; Data analysis; Datasets; Emission analysis; FDG-PET; Fluorodeoxyglucose F18; Hemispheres; Identification methods; Image Processing, Computer-Assisted - methods; Infarction, Middle Cerebral Artery - diagnostic imaging; Injury analysis; Intensity normalization; Internal Medicine; Ischemia; Linear regression; Linearity; Male; MCAO; Medical imaging; Methods; Occlusion; Other; Positron emission; Positron emission tomography; Positron-Emission Tomography - methods; Rats; Rats, Sprague-Dawley; Regression models; Similarity coefficient method; Stroke; Stroke - diagnostic imaging; Triphenyltetrazolium chloride; FDG-PET; MCAO; Linear regression; Intensity normalization
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.109401

In the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET) images, intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of the most appropriate normalization method in stroke studies remains unclear, as demonstrated by inconsistent findings in the literature. Here, we propose a regression- and single-subject-based model for analyzing FDG-PET images without intensity normalization. Two independent data sets were collected before and after middle cerebral artery occlusion (MCAO), with one comprising 120 rats and the other 96 rats. After data preprocessing, voxel intensities in the same region and hemisphere were paired before and after the MCAO scan. A linear regression model was applied to the paired data, and the coefficient of determination R2 was calculated to measure the linearity. The R2 values between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those with reduced linearity. Our method was compared with voxel-wise analysis under different intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data. The significant regions identified by the proposed method showed a large degree of overlap with the infarcted regions identified by TTC data, as measured by the Dice similarity coefficient (DSC). The average DSC of the proposed method was 59.7%, whereas the DSCs of the existing approaches ranged from 41.4%∼51.3%. Additional validation using receiver operating characteristic (ROC) demonstrated that the area under the curve (AUC) of the average ROC curves reached 0.84 using the proposed method, whereas existing methods achieved AUCs ranging from 0.77∼0.79. The identified regions were consistent across the two independent data sets, and some findings were corroborated by other publications. The proposed model presents a novel quantitative approach for identifying injury regions post-stroke using FDG-PET images. The calculation does not require intensity normalization and can be applied to individual subjects. The method yields more sensitive results compared to existing identification methods. •Identify injured regions after cerebral ischemia using a regression model.•Provide better sensitivity than voxel-wise analysis with intensity normalization.•Use TTC staining-based ground truth to validate the performance.•Significant regions can be derived from a single subject.