A comparison of 4D flow MRI-derived wall shear stress with computational fluid dynamics methods for intracranial aneurysms and carotid bifurcations - A review
- PMID: 29223732
- DOI: 10.1016/j.mri.2017.12.005
A comparison of 4D flow MRI-derived wall shear stress with computational fluid dynamics methods for intracranial aneurysms and carotid bifurcations - A review
Abstract
Background: 4D flow MRI is a relatively quick method for obtaining wall shear stress (WSS) in vivo, a hemodynamic parameter which has shown promise in risk stratification for rupture of cerebrovascular diseases such as intracranial aneurysms and atherosclerotic plaques. The accuracy of such measurements is still largely unknown.
Objective: To quantify the accuracy of 4D flow MRI-derived wall shear stress values for intracranial aneurysms and carotid bifurcations.
Method: We performed a review of all original research articles which compared the magnitudes of WSS derived from 4D flow MRI with corresponding values derived from computational fluid dynamics (CFD) within both intracranial aneurysms and carotid bifurcations.
Result: For intracranial aneurysms and carotid bifurcations, 4D flow MRI-derived WSS estimations are generally lower in magnitude compared to WSS derived by CFD methods. These differences are more pronounced in regions of higher WSS. However, the relative distributions of WSS derived from both methods are reasonably similar.
Conclusion: Pooled analysis suggests that WSS magnitudes obtained by 4D flow MRI are underestimated, while the relative distribution is reasonably accurate, the latter being an important factor for determining the natural history of intracranial aneurysms and other cerebrovascular diseases. 4D flow MRI shows enormous potential in providing new risk stratification parameters which could have significant impact on individualized treatment decisions and improved patient outcomes.
Keywords: 4D flow MRI; Aneurysm; Carotid bifurcation; Computational fluid dynamics; Intracranial; Wall shear stress.
Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.
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