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Abstract

Bicuspid aortic valve (BAV), i.e. the fusion of two aortic valve cusps, is the most frequent congenital cardiac malformation. Its progression is often characterized by accelerated leaflet calcification and aortic wall dilation. These processes are likely enhanced by altered biomechanical stimuli, including fluid-dynamic wall shear stresses (WSS) acting on both the aortic wall and the aortic valve. Several studies have proposed the exploitation of 4D-flow magnetic resonance imaging sequences to characterize abnormalin vivoWSS in BAV-affected patients, to support prognosis and timing of intervention. However, current methods fail to quantify WSS peak values.

On this basis, we developed two new methods for the improved quantification ofin vivoWSS acting on the aortic wall based on 4D-flow data.

We tested both methods separately and in combination on synthetic datasets obtained by two computational fluid-dynamics (CFD) models of the aorta with healthy and bicuspid aortic valve. Tests highlighted the need for data spatial resolution at least comparable to current clinical guidelines, the low sensitivity of the methods to data noise, and their capability, when used jointly, to compute more realistic peak WSS values as compared to state-of-the-art methods.

The integrated application of the two methods on the real 4D-flow data from a preliminary cohort of three healthy volunteers and three BAV-affected patients confirmed these indications. In particular, quantified WSS peak values were one order of magnitude higher than those reported in previous 4D-flow studies, and much closer to those computed by highly time- and space-resolved CFD simulations.

Details

Title
Towards the improved quantification of in vivo abnormal wall shear stresses in BAV-affected patients from 4D-flow imaging: Benchmarking and application to real data
Publication title
Volume
50
Pages
93-101
Publication year
2017
Publication date
2017
Publisher
Elsevier Limited
Place of publication
Kidlington
Country of publication
United Kingdom
ISSN
00219290
e-ISSN
18732380
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
ProQuest document ID
1885095726
Document URL
https://www.proquest.com/scholarly-journals/towards-improved-quantification-vivo-abnormal/docview/1885095726/se-2?accountid=208611
Copyright
Copyright Elsevier Limited 2017
Last updated
2025-05-01
Database
ProQuest One Academic