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

Wang, Min; Cui, Bixiao; Shan, Yi; Yang, Hongwei; Yan, Zhuangzhi; Sundar, Lalith Kumar Shiyam; Alberts, Ian; Rominger, Axel; Wendler, Thomas; Shi, Kuangyu; Ma, Yan; Jiang, Jiehui; Lu, Jie (2022). Non-Invasive Glucose Metabolism Quantification Method Based on Unilateral ICA Image Derived Input Function by Hybrid PET/MR in Ischemic Cerebrovascular Disease. IEEE journal of biomedical and health informatics, 26(10), pp. 5122-5129. Institute of Electrical and Electronics Engineers 10.1109/JBHI.2022.3193190

Text Non-Invasive_Glucose_Metabolism_Quantification_Method_Based_on_Unilateral_ICA_Image_Derived_Input_Function_by_Hybrid_PET_MR_in_Ischemic_Cerebrovascular_Disease.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (10MB)

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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