Three-dimensional aneurysm wall enhancement in fusiform intracranial aneurysms is associated with aneurysmal symptoms.

MRI aneurysm wall enhancement inflammation intracranial aneurysm three-dimensional

Journal

Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481

Informations de publication

Date de publication:
2023
Historique:
received: 22 02 2023
accepted: 18 04 2023
medline: 22 5 2023
pubmed: 22 5 2023
entrez: 22 5 2023
Statut: epublish

Résumé

Aneurysm wall enhancement (AWE) in high-resolution magnetic resonance imaging (HR-MRI) is a potential biomarker for evaluating unstable aneurysms. Fusiform intracranial aneurysms (FIAs) frequently have a complex and curved structure. We aimed to develop a new three-dimensional (3D) aneurysmal wall enhancement (AWE) characterization method to enable comprehensive FIA evaluation and to investigate the ability of 3D-AWE to predict symptomatic FIA. We prospectively recruited patients with unruptured FIAs and received 3 T HR-MRI imaging from September 2017 to January 2019. 3D models of aneurysms and parent arteries were generated. Boundaries of the FIA were determined using 3D vessel diameter measurements. D Forty-seven patients with 47 FIAs were included. Mean patient age was 55 ± 12.62 years and 74.5% were male. Twenty-nine patients (38.3%) were symptomatic. After adjusting for baseline differences in age, hypertension, L The new 3D AWE method, which enables the use of numerous new metrics, can predict symptomatic FIAs. Different 3D-AWE between the three FIA subtypes may be helpful in understanding the pathophysiology of FIAs.

Sections du résumé

Background and purpose UNASSIGNED
Aneurysm wall enhancement (AWE) in high-resolution magnetic resonance imaging (HR-MRI) is a potential biomarker for evaluating unstable aneurysms. Fusiform intracranial aneurysms (FIAs) frequently have a complex and curved structure. We aimed to develop a new three-dimensional (3D) aneurysmal wall enhancement (AWE) characterization method to enable comprehensive FIA evaluation and to investigate the ability of 3D-AWE to predict symptomatic FIA.
Methods UNASSIGNED
We prospectively recruited patients with unruptured FIAs and received 3 T HR-MRI imaging from September 2017 to January 2019. 3D models of aneurysms and parent arteries were generated. Boundaries of the FIA were determined using 3D vessel diameter measurements. D
Results UNASSIGNED
Forty-seven patients with 47 FIAs were included. Mean patient age was 55 ± 12.62 years and 74.5% were male. Twenty-nine patients (38.3%) were symptomatic. After adjusting for baseline differences in age, hypertension, L
Conclusion UNASSIGNED
The new 3D AWE method, which enables the use of numerous new metrics, can predict symptomatic FIAs. Different 3D-AWE between the three FIA subtypes may be helpful in understanding the pathophysiology of FIAs.

Identifiants

pubmed: 37214386
doi: 10.3389/fnins.2023.1171946
pmc: PMC10196058
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1171946

Informations de copyright

Copyright © 2023 Chen, Peng, Liu, Xia, Niu, He, Xu, Bai, Li, Xu, Duan, Sui, Zhao and Liu.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Xuge Chen (X)

Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital University, Beijing, China.

Fei Peng (F)

Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital University, Beijing, China.

Xinmin Liu (X)

Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Jiaxiang Xia (J)

Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital University, Beijing, China.

Hao Niu (H)

Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital University, Beijing, China.

Xiaoxin He (X)

Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital University, Beijing, China.

Boya Xu (B)

Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital University, Beijing, China.

Xiaoyan Bai (X)

Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Zhiye Li (Z)

Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Peng Xu (P)

Tiantan Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing, China.

Yonghong Duan (Y)

Department of Neurosurgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

Binbin Sui (B)

Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Tiantan Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing, China.

Xingquan Zhao (X)

Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Aihua Liu (A)

Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital University, Beijing, China.

Classifications MeSH