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.

Public Health Relevance

The majority of brain aneurysms are treated, despite the fact that most of them have very low risk or rupture. Studies indicated that brain aneurysm risk factors include a range of clinical (e.g., patient?s age, sex, family history) and anatomical (e.g., aneurysm location, size and shape) parameters, as well as local blood flow dynamics. The proposed studies will determine the specific contribution of blood flow variables for improving the risk assessment of brain aneurysms and examine whether these variables can be reliably calculated from flow velocities measured with phase-contrast magnetic resonance imaging.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Clinical Translational Imaging Science Study Section (CTIS)
Program Officer
Luo, James
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Purdue University
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
West Lafayette
United States
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Baghaie, Ahmadreza; Schnell, Susanne; Bakhshinejad, Ali et al. (2018) Curvelet Transform-based volume fusion for correcting signal loss artifacts in Time-of-Flight Magnetic Resonance Angiography data. Comput Biol Med 99:142-153
Bakhshinejad, Ali; Baghaie, Ahmadreza; Vali, Alireza et al. (2017) Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression. J Biomech 58:162-173
Vali, Alireza; Abla, Adib A; Lawton, Michael T et al. (2017) Computational Fluid Dynamics modeling of contrast transport in basilar aneurysms following flow-altering surgeries. J Biomech 50:195-201
Walcott, Brian P; Reinshagen, Clemens; Stapleton, Christopher J et al. (2016) Predictive modeling and in vivo assessment of cerebral blood flow in the management of complex cerebral aneurysms. J Cereb Blood Flow Metab 36:998-1003
Lawton, Michael T; Abla, Adib A; Rutledge, W Caleb et al. (2016) Bypass Surgery for the Treatment of Dolichoectatic Basilar Trunk Aneurysms: A Work in Progress. Neurosurgery 79:83-99
Rayz, V L; Abla, A; Boussel, L et al. (2015) Computational modeling of flow-altering surgeries in basilar aneurysms. Ann Biomed Eng 43:1210-22