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颅内动脉瘤的血液动力学:连接医学成像和生物力学分析。

Hemodynamics of Cerebral Aneurysms: Connecting Medical Imaging and Biomechanical Analysis.

机构信息

Weldon School of Biomedical Engineering and School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907, USA; email:

Department of Neurosurgery, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.

出版信息

Annu Rev Biomed Eng. 2020 Jun 4;22:231-256. doi: 10.1146/annurev-bioeng-092419-061429. Epub 2020 Mar 25.

Abstract

In the last two decades, numerous studies have conducted patient-specific computations of blood flow dynamics in cerebral aneurysms and reported correlations between various hemodynamic metrics and aneurysmal disease progression or treatment outcomes. Nevertheless, intra-aneurysmal flow analysis has not been adopted in current clinical practice, and hemodynamic factors usually are not considered in clinical decision making. This review presents the state of the art in cerebral aneurysm imaging and image-based modeling, discussing the advantages and limitations of each approach and focusing on the translational value of hemodynamic analysis. Combining imaging and modeling data obtained from different flow modalities can improve the accuracy and fidelity of resulting velocity fields and flow-derived factors that are thought to affect aneurysmal disease progression. It is expected that predictive models utilizing hemodynamic factors in combination with patient medical history and morphological data will outperform current risk scores and treatment guidelines. Possible future directions include novel approaches enabling data assimilation and multimodality analysis of cerebral aneurysm hemodynamics.

摘要

在过去的二十年中,许多研究都对脑动脉瘤中的血流动力学进行了患者特异性计算,并报告了各种血流动力学指标与动脉瘤疾病进展或治疗结果之间的相关性。然而,目前的临床实践并未采用瘤内血流分析,临床决策通常也不考虑血流动力学因素。本文综述了脑动脉瘤成像和基于图像的建模的最新进展,讨论了每种方法的优缺点,并重点介绍了血流分析的转化价值。结合来自不同流动模式的成像和建模数据,可以提高所得速度场和被认为影响动脉瘤疾病进展的血流衍生因素的准确性和逼真度。预计利用血流动力学因素结合患者病史和形态学数据的预测模型将优于当前的风险评分和治疗指南。可能的未来方向包括使脑动脉瘤血流动力学数据同化和多模态分析的新方法。

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