Evaluation of cerebral perfusion heterogeneity by the electrical impedance tomography

PurposeThe purpose of this study was to evaluate the ability of global inhomogeneity index (GI) and left-right asymmetry index (AI) based on electrical impedance tomography (EIT) to be used in assessing cerebral perfusion heterogeneity. The diagnostic value of these two indices in identifying abnorm...

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Main Authors: Mingxu Zhu, Yu Wang, Junyao Li, Weice Wang, Guobin Gao, Zhenyu Ji, Benyuan Liu, Lei Wang, Weichen Li, Xuetao Shi
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-11-01
Series:Frontiers in Physiology
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Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2024.1476040/full
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author Mingxu Zhu
Mingxu Zhu
Yu Wang
Junyao Li
Junyao Li
Weice Wang
Weice Wang
Guobin Gao
Zhenyu Ji
Zhenyu Ji
Benyuan Liu
Benyuan Liu
Lei Wang
Weichen Li
Xuetao Shi
Xuetao Shi
author_facet Mingxu Zhu
Mingxu Zhu
Yu Wang
Junyao Li
Junyao Li
Weice Wang
Weice Wang
Guobin Gao
Zhenyu Ji
Zhenyu Ji
Benyuan Liu
Benyuan Liu
Lei Wang
Weichen Li
Xuetao Shi
Xuetao Shi
author_sort Mingxu Zhu
collection DOAJ
description PurposeThe purpose of this study was to evaluate the ability of global inhomogeneity index (GI) and left-right asymmetry index (AI) based on electrical impedance tomography (EIT) to be used in assessing cerebral perfusion heterogeneity. The diagnostic value of these two indices in identifying abnormalities in the degree of cerebral perfusion heterogeneity was also explored.MethodsIn this study, Transcranial Doppler (TCD) was used as a control, and unilateral carotid artery was compressed to change the degree of heterogeneity of cerebral perfusion in 15 healthy volunteers. The control group consisted of an additional 15 volunteers without any intervention. EIT perfusion images were obtained by calculating the impedance difference between at the beginning and end of cerebral vasodilation. Subsequently, GI and AI were calculated based on the pixel values of intracranial regions.ResultsThe GI and AI values in the non-carotid artery compression (NCAC) group were significantly lower than those in the unilateral carotid artery compression (UCAC) group (P < 0.001), whereas there was no significant difference between the left carotid artery compression (LCAC) and right carotid artery compression (RCAC) groups. ROC analysis showed that the area under the curve (AUC), specificity and sensitivity of GI in distinguishing between NCAC and UCAC were 0.94, 0.90 and 0.87, respectively. The AUC, specificity and sensitivity of AI in distinguishing between NCAC and UCAC were 0.86, 0.87 and 0.73, respectively.ConclusionThe results demonstrated that the GI and AI effectively quantify the distribution of intracranial perfusion, demonstrating excellent validity and interindividual comparability, and the ability to detect abnormal cerebral perfusion heterogeneity.
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spelling doaj-art-3e160ff87af94f18be6ef9735c5f02742025-08-20T02:14:56ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2024-11-011510.3389/fphys.2024.14760401476040Evaluation of cerebral perfusion heterogeneity by the electrical impedance tomographyMingxu Zhu0Mingxu Zhu1Yu Wang2Junyao Li3Junyao Li4Weice Wang5Weice Wang6Guobin Gao7Zhenyu Ji8Zhenyu Ji9Benyuan Liu10Benyuan Liu11Lei Wang12Weichen Li13Xuetao Shi14Xuetao Shi15Department of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaShaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Department of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaInstitute of Medical Research, Northwestern Polytechnical University, Xi’an, ChinaDepartment of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaShaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Department of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaDepartment of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaShaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Department of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaFaculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, ChinaDepartment of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaShaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Department of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaDepartment of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaShaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Department of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaInstitute of Medical Research, Northwestern Polytechnical University, Xi’an, ChinaFunctional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an, ChinaDepartment of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaShaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Department of Biomedical Engineering, Air Force Medical University, Xi’an, ChinaPurposeThe purpose of this study was to evaluate the ability of global inhomogeneity index (GI) and left-right asymmetry index (AI) based on electrical impedance tomography (EIT) to be used in assessing cerebral perfusion heterogeneity. The diagnostic value of these two indices in identifying abnormalities in the degree of cerebral perfusion heterogeneity was also explored.MethodsIn this study, Transcranial Doppler (TCD) was used as a control, and unilateral carotid artery was compressed to change the degree of heterogeneity of cerebral perfusion in 15 healthy volunteers. The control group consisted of an additional 15 volunteers without any intervention. EIT perfusion images were obtained by calculating the impedance difference between at the beginning and end of cerebral vasodilation. Subsequently, GI and AI were calculated based on the pixel values of intracranial regions.ResultsThe GI and AI values in the non-carotid artery compression (NCAC) group were significantly lower than those in the unilateral carotid artery compression (UCAC) group (P < 0.001), whereas there was no significant difference between the left carotid artery compression (LCAC) and right carotid artery compression (RCAC) groups. ROC analysis showed that the area under the curve (AUC), specificity and sensitivity of GI in distinguishing between NCAC and UCAC were 0.94, 0.90 and 0.87, respectively. The AUC, specificity and sensitivity of AI in distinguishing between NCAC and UCAC were 0.86, 0.87 and 0.73, respectively.ConclusionThe results demonstrated that the GI and AI effectively quantify the distribution of intracranial perfusion, demonstrating excellent validity and interindividual comparability, and the ability to detect abnormal cerebral perfusion heterogeneity.https://www.frontiersin.org/articles/10.3389/fphys.2024.1476040/fullcerebral perfusionelectrical impedance tomographyinhomogeneity indexasymmetry indexheterogeneity
spellingShingle Mingxu Zhu
Mingxu Zhu
Yu Wang
Junyao Li
Junyao Li
Weice Wang
Weice Wang
Guobin Gao
Zhenyu Ji
Zhenyu Ji
Benyuan Liu
Benyuan Liu
Lei Wang
Weichen Li
Xuetao Shi
Xuetao Shi
Evaluation of cerebral perfusion heterogeneity by the electrical impedance tomography
Frontiers in Physiology
cerebral perfusion
electrical impedance tomography
inhomogeneity index
asymmetry index
heterogeneity
title Evaluation of cerebral perfusion heterogeneity by the electrical impedance tomography
title_full Evaluation of cerebral perfusion heterogeneity by the electrical impedance tomography
title_fullStr Evaluation of cerebral perfusion heterogeneity by the electrical impedance tomography
title_full_unstemmed Evaluation of cerebral perfusion heterogeneity by the electrical impedance tomography
title_short Evaluation of cerebral perfusion heterogeneity by the electrical impedance tomography
title_sort evaluation of cerebral perfusion heterogeneity by the electrical impedance tomography
topic cerebral perfusion
electrical impedance tomography
inhomogeneity index
asymmetry index
heterogeneity
url https://www.frontiersin.org/articles/10.3389/fphys.2024.1476040/full
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