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Risk Assessment of Cerebral Aneurysm Growth with 4D flow MRI

使用 4D 流 MRI 评估脑动脉瘤生长的风险

基本信息

批准号：
10673860
负责人：
Sameer A Ansari
金额：
$ 64.22万
依托单位：
PURDUE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-03 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10673860
关键词：
4D MRI Affect Age Agreement Anatomy Aneurysm Angiography Blood flow Brain Aneurysms California Cardiovascular system Cerebral Aneurysm Cerebrovascular Circulation Clinical Clinical Data Collaborations Communities Computer Models Computing Methodologies Data Databases Deposition Engineering Ensure Experimental Models Family Funding Future Goals Growth Guidelines Hypertension Image In Vitro Institution Intervention Intracranial Aneurysm Liquid substance Location Magnetic Resonance Imaging Measurement Measures Mechanics Methodology Methods Modeling Monitor Morbidity - disease rate Morphology Multiparametric Analysis Neurologic Neurosurgeon Noise Operative Surgical Procedures Outcome Patients Phase Physics Postoperative Period Probability Recording of previous events Reproducibility Research Resolution Resources Risk Risk Assessment Risk Factors Role Rupture Ruptured Aneurysm San Francisco Shapes Site Statistical Data Interpretation Statistical Models Techniques Thrombus Time United States National Institutes of Health Universities Velocimetries Work artificial intelligence method clinical center clinical risk comorbidity data sharing diagnostic tool enhancing factor follow-up hemodynamics imaging study improved in vivo indexing mortality neurovascular novel strategies particle predictive modeling prospective residence risk stratification serial imaging sex shear stress success

项目摘要

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.

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数据将用于本研究。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。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Computational modeling of flow-altering surgeries in basilar aneurysms.

DOI：
10.1007/s10439-014-1170-x
发表时间：
2015-05
期刊：
ANNALS OF BIOMEDICAL ENGINEERING
影响因子：
3.8
作者：
Rayz, V. L.;Abla, A.;Boussel, L.;Leach, J. R.;Acevedo-Bolton, G.;Saloner, D.;Lawton, M. T.
通讯作者：
Lawton, M. T.

Computational Fluid Dynamics modeling of contrast transport in basilar aneurysms following flow-altering surgeries.

改变血流手术后基底动脉瘤中造影剂传输的计算流体动力学模型。