Risk Assessment of Cerebral Aneurysm Growth with 4D flow MRI

项目摘要 本项目的目标是确定血流动力学因素对未破裂血管的风险评估的贡献。 颅内动脉瘤（UIA），并根据增强的体内4D流动MRI数据计算这些因素。即使 大多数UIA是稳定的，大多数UIA患者由于严重的风险而接受介入治疗 如果动脉瘤破裂的话。以前的研究表明，除了临床（例如，年龄性别 合并症）和形态学（例如，位置和大小）因素，UIA进展受局部血流影响 动力学与UIA生长相关的血流动力学因素可以通过计算和 实验模型或4D流动MRI测量;然而，每种方法都有局限性。前一 NIH资助的项目，重点是开发基于图像的计算方法，用于预测术后血流 干预后。此次更新的目标是使用已开发的框架来改进风险分层 使用基于图像的流分析。拟议项目将开发多参数预测模型 该联合收割机将临床和形态学因素与根据增强的4D血流计算的血流动力学因素相结合 MRI数据。不同模型预测的乌干达投资管理局增长将与纵向观察结果进行比较。 影像学研究因此，拟议项目的目的是：（1）确定乌干达投资管理局增长的可能性 通过利用形态学和临床因素以及从计算获得的血流动力学因素， a）基于形态学和临床因素进行统计分析 从纵向成像获得，以及B）通过包括血液动力学因素来扩展统计模型 （2）通过a）确定4D血流再现性， 和变异性与体外研究，和B）应用先进的数据扩增方法，以提高 计算影响动脉瘤生长的血流动力学因素的准确性;（3）确定UIA的概率 基于多参数分析的生长，利用从增强的4D流动MRI计算的血液动力学因素。 该项目的成功完成将解决关于血流动力学因素如何影响的争议 动脉瘤生长，并建立4D流动MRI作为UIA风险分层的诊断工具。 这个合作项目涉及普渡大学的心血管工程小组， 西北大学、加州大学旧金山分校的神经外科医生、神经放射学家和MRI物理学家 弗朗西斯科和巴罗神经研究所。这个跨学科的团队将汇集专家， 神经血管手术，MRI测速，患者特定流量计算，实验流体力学和 统计分析从这些一流的临床中心获得的回顾性和前瞻性UIA数据将 在这项研究中使用。普渡大学优秀的工程资源和世界一流的成像技术 资源在西北，加州大学旧金山弗朗西斯科和巴罗，以及现有的数据共享协议 这些机构之间的合作以及PI之间的持续合作将确保该项目的成功。

PROJECT SUMMARY The goal of this project is to determine the contribution of hemodynamic factors to risk assessment of unruptured intracranial aneurysms (UIAs) and calculate these factors from enhanced in vivo 4D flow MRI data. Even though most UIAs are stable, the majority of UIA patients are offered interventional treatment due to the grave risk presented if an aneurysm ruptures. Previous studies indicated that in addition to clinical (e.g., age, sex, comorbidities) and morphological (e.g., location and size) factors, UIA progression is affected by local blood flow dynamics. Hemodynamic factors associated with UIA growth can be obtained from computational and experimental models or from 4D flow MRI measurements; however, each approach has limitations. The previous NIH-funded project focused on developing image-based computational methods for predicting postoperative flow following interventions. The goal of this renewal is to use the developed framework to improve risk stratification of UIAs using image-based flow analysis. The proposed project will develop multi-parametric predictive models that combine clinical and morphological factors with hemodynamic factors calculated from augmented 4D flow MRI data. The UIA growth predicted by different models will be compared to outcomes observed in longitudinal imaging studies. The aims of the proposed project are, therefore, to: (1) determine the probability of UIA growth by utilizing morphological and clinical factors together with hemodynamic factors obtained from computational and experimental flow models by a) performing statistical analysis based on morphological and clinical factors obtained from longitudinal imaging, and b) extending statistical model by including hemodynamic factors computed from patient-specific models; (2) Enhance 4D flow MRI data by a) determining 4D flow reproducibility and variability with in vitro studies, and b) applying advanced data augmentation methods to improve the accuracy of calculated hemodynamic factors affecting aneurysm growth; (3) determine the probability of UIA growth based on multi-parametric analysis utilizing hemodynamic factors calculated from enhanced 4D flow MRI. Successful completion of the project will resolve the controversy regarding how hemodynamic factors affect aneurysm growth and establish 4D flow MRI as a diagnostic tool for UIA risk stratification. This collaborative project engages the cardiovascular engineering group at Purdue University and neurosurgeons, neuroradiologists and MRI physicists at Northwestern University, University of California San Francisco and Barrow Neurological Institute. This cross-disciplinary team will bring together experts in neurovascular surgeries, MRI velocimetry, patient-specific flow computations, experimental fluid mechanics and statistical analysis. Retrospective and prospective UIAs data obtained from these superb clinical centers will be used in this study. The outstanding engineering resources available at Purdue and world-class imaging resources at Northwestern, UC San Francisco and Barrow, as well as existing the data sharing agreements between these institutions and ongoing collaborations between the PIs, will ensure the project's success.