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Anat Sci Int. 2025 Jun;100(3):370-374. doi: 10.1007/s12565-024-00818-x. Epub 2024 Dec 23.
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Prognostic value of morphology and hemodynamics in moyamoya disease for long-term outcomes and disease progression.形态学和血流动力学在烟雾病长期结局和疾病进展中的预后价值。
Sci Rep. 2024 Nov 15;14(1):28182. doi: 10.1038/s41598-024-79608-4.
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Self-supervised learning for accurately modelling hierarchical evolutionary patterns of cerebrovasculature.基于自监督学习的脑循环系统进化模式精准建模。
Nat Commun. 2024 Oct 25;15(1):9235. doi: 10.1038/s41467-024-53550-5.
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Combining Computational Fluid Dynamics, Structural Analysis, and Machine Learning to Predict Cerebrovascular Events: A Mild ML Approach.结合计算流体动力学、结构分析和机器学习来预测脑血管事件:一种轻度机器学习方法。
Diagnostics (Basel). 2024 Oct 2;14(19):2204. doi: 10.3390/diagnostics14192204.
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Distal activity patterns shape the spatial specificity of neurovascular coupling.远侧活动模式塑造了神经血管耦合的空间特异性。
Nat Neurosci. 2024 Nov;27(11):2101-2114. doi: 10.1038/s41593-024-01756-7. Epub 2024 Sep 4.
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High-Resolution MRA Cerebrovascular Findings in a Tri-Ethnic Population.三人群组高分辨率磁共振血管成像的脑血管研究结果。
AJNR Am J Neuroradiol. 2024 Nov 7;45(11):1661-1669. doi: 10.3174/ajnr.A8402.
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Chemical and perfusion markers as predictors of moyamoya disease progression and complication types.化学和灌注标志物作为烟雾病进展和并发症类型的预测因子。
Sci Rep. 2024 Jan 2;14(1):56. doi: 10.1038/s41598-023-47984-y.
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End to end stroke triage using cerebrovascular morphology and machine learning.利用脑血管形态学和机器学习进行端到端的卒中分诊
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9
CASMatching strategy for automated detection and quantification of carotid artery stenosis based on digital subtraction angiography.基于数字减影血管造影的颈动脉硬化自动检测与定量的 CAS 匹配策略。
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10
Report from the society of magnetic resonance angiography: clinical applications of 7T neurovascular MR in the assessment of intracranial vascular disease.来自磁共振血管造影协会的报告:7T 神经血管磁共振在颅内血管疾病评估中的临床应用。
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脑血管形态学:对正常变异、衰老影响及疾病意义的洞察

Cerebrovascular morphology: Insights into normal variations, aging effects, and disease implications.

作者信息

Deshpande Aditi, Zhang Lucy Q, Balu Ramani, Yahyavi-Firouz-Abadi Noushin, Badjatia Neeraj, Laksari Kaveh, Tahsili-Fahadan Pouya

机构信息

Department of Mechanical Engineering, University of California, Riverside, USA.

Department of Neurology, Duke University School of Medicine, Durham, NC, USA.

出版信息

J Cereb Blood Flow Metab. 2025 May 2:271678X251328537. doi: 10.1177/0271678X251328537.

DOI:10.1177/0271678X251328537
PMID:40314210
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12048404/
Abstract

Cerebrovascular morphology plays a critical role in brain health, influencing cerebral blood flow (CBF) and contributing to the pathogenesis of various neurological diseases. This review examines the anatomical structure of the cerebrovascular network and its variations in healthy and diseased populations and highlights age-related changes and their implications in various neurological conditions. Normal variations, including the completeness and anatomical anomalies of the Circle of Willis and collateral circulation, are discussed in relation to their impact on CBF and susceptibility to ischemic events. Age-related changes in the cerebrovascular system, such as alterations in vessel geometry and density, are explored for their contributions to age-related neurological disorders, including Alzheimer's disease and vascular dementia. Advances in medical imaging and computational methods have enabled automatic quantitative assessment of cerebrovascular structures, facilitating the identification of pathological changes in both acute and chronic cerebrovascular disorders. Emerging technologies, including machine learning and computational fluid dynamics, offer new tools for predicting disease risk and patient outcomes based on vascular morphology. This review underscores the importance of understanding cerebrovascular remodeling for early diagnosis and the development of novel therapeutic approaches in brain diseases.

摘要

脑血管形态在脑健康中起着关键作用,影响脑血流量(CBF)并促成各种神经疾病的发病机制。本综述探讨了脑血管网络的解剖结构及其在健康人群和患病群体中的变化,并强调了与年龄相关的变化及其在各种神经疾病中的影响。讨论了正常变异,包括 Willis 环的完整性和解剖异常以及侧支循环,及其对 CBF 和缺血事件易感性的影响。探讨了脑血管系统中与年龄相关的变化,如血管几何形状和密度的改变,及其对包括阿尔茨海默病和血管性痴呆在内的与年龄相关的神经疾病的影响。医学成像和计算方法的进展使得能够对脑血管结构进行自动定量评估,有助于识别急性和慢性脑血管疾病中的病理变化。包括机器学习和计算流体动力学在内的新兴技术为基于血管形态预测疾病风险和患者预后提供了新工具。本综述强调了了解脑血管重塑对于脑部疾病的早期诊断和新治疗方法开发的重要性。