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Non-Invasive Glucose Metabolism Quantification Method Based on Unilateral ICA Image Derived Input Function by Hybrid PET/MR in Ischemic Cerebrovascular Disease

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机构: [1]School of Life Science, Shanghai University, Shanghai 200444, China [2]Department of Radiology and Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing 100053, China [3]Quantitative Imaging and Medical Physics, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna 1090, Austria [4]Department of Nuclear Medicine. Inselspital, Bern University Hospital, University of Bern, Bern 3012, Switzerland [5]Chair for Computer-Aided Medical Procedures and Augmented Reality, Technical University of Munich, Munich 85748, Germany
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关键词: Glucose Biochemistry Surgery Imaging Diseases Magnetic resonance imaging Image segmentation Glucose metabolism ischemic cerebrovascular disease noninvasive quantification PET MR vascular territory

摘要:
The non-invasive quantification of the cerebral metabolic rate for glucose (CMRGlc) and the characterization of cerebral metabolism in the cerebrovascular territories are helpful in understanding ischemic cerebrovascular disease (ICVD). Firstly, we investigated a non-invasive quantification approach based on an image-derived input function (IDIF) in ICVD. Second, we studied the metabolic changes in CMRGlc after surgical intervention. We evaluated the hypothesis that the IDIF method based on the unilateral internal carotid artery could address challenges in ICVD quantification. The CMRGlc and standardized uptake value ratio (SUVR) were used to measure glucose metabolism activity. Healthy controls showed no significant differences in CMRGlc values between bilateral and unilateral IDIF measurements (intraclass correlation coefficient [ICC]: 0.91-0.98). Patients with ICVD showed significantly increased CMRGlc values after surgical intervention for all territories (percentage changes: 7.4%-22.5%). In contrast, SUVR showed minor differences between postoperative and preoperative patients, indicating that it was a poor biomarker for the diagnosis of ICVD. A significant association between CMRGlc and the National Institutes of Health Stroke Scale (NIHSS) scores was observed (r=-0.54). Our findings suggested that IDIF could be a valuable tool for CMRGlc quantification in patients with ICVD and may advance personalized precision interventions.

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基金编号: 81974261 82130058 82020108013 QNPY2021037 2017SHZDZX01 2018SHZDZX03 D20031

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出版当年[2021]版:
大类 | 2 区 工程技术
小类 | 1 区 数学与计算生物学 1 区 医学:信息 2 区 计算机:信息系统 2 区 计算机:跨学科应用
最新[2023]版:
大类 | 2 区 医学
小类 | 1 区 计算机:信息系统 1 区 数学与计算生物学 2 区 计算机:跨学科应用 2 区 医学:信息
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出版当年[2020]版:
Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Q1 MEDICAL INFORMATICS Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
最新[2023]版:
Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Q1 MEDICAL INFORMATICS

影响因子: 最新[2023版] 最新五年平均 出版当年[2020版] 出版当年五年平均 出版前一年[2019版] 出版后一年[2021版]

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第一作者机构: [1]School of Life Science, Shanghai University, Shanghai 200444, China
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