Voxel-wise regression without intensity normalization provides better sensitivity when identifying injury regions after stroke in FDG-PET images

Author:

He WuxianORCID,Tang Hongtu,Li Jia,Shen Xiaoyan,Zhang Xuechen,Li Chenrui,Liu Huafeng,Yu WeichuanORCID

Abstract

AbstractBackgroundIn the analysis of brain fluorodeoxyglucose positron emission tomography (FDG-PET), intensity normalization is a necessary step to reduce inter-subject variability. However, the choice of an appropriate normalization method in stroke studies remains unclear, leading to inconsistent findings in the literature.Materials and methodsHere, we propose a regression-based 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 comprising 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 was calculated to measure the linearity. The results between the ipsilateral and contralateral hemispheres were compared, and significant regions were defined as those having reduced linearity. Our method was compared with existing intensity normalization methods and validated using the triphenyl tetrazolium chloride (TTC) staining data.ResultsThe proposed method detected more injury areas compared to existing approaches, as confirmed by the ground truth provided by TTC. The area under the curve (AUC) of the average receiver operating characteristic (ROC) curves using our method reached 0.84, whereas the AUCs using existing methods ranged from 0.77 ∼ 0.79. The average false positive rate (FPR) and true positive rate (TPR) of the individual analysis results using our method (FPR = 0.06, TPR = 0.56) were better than the group-wise analysis results usingt-tests (FPR = 0.10, TPR = 0.51). The identified injury regions were consistent in the two independent data sets. Some of them were confirmed by other publications.ConclusionsThe proposed method offers a new quantitative approach to analyzing FDG-PET images. The calculation does not involve intensity normalization and can be applied to a single subject. The method yields more sensitive results than existing intensity normalization methods.

Publisher

Cold Spring Harbor Laboratory

Reference60 articles.

1. How to Estimate Global Activity Independent of Changes in Local Activity

2. Imaging of a Clinically Relevant Stroke Model

3. The spatiotemporal organization of auditory, visual, and auditory-visual evoked potentials in rat cortex

4. Alternative normalization methods demonstrate widespread cortical hypometabolism in untreated de novo Parkinson’s disease;The Quarterly Journal of Nuclear Medicine and Molecular Imaging,2012

5. Standards for PET Image Acquisition and Quantitative Data Analysis

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3