Aortic flow patterns before and after personalised external aortic root support implantation in Marfan patients. Issue 1 (4th January 2016)

• Record Type: Journal Article
• Authors: Singh, S.D.; Xu, X.Y.; Wood, N.B.; Pepper, J.R.; Izgi, C.; Treasure, T.; Mohiaddin, R.H.
• Is Part Of: Journal of biomechanics. Volume 49:Issue 1(2016)
• Page Start: 100
• Page End: 111
• Publication Date: 2016-01-04
• Subjects: Personalised external aortic root support (PEARS) -- Computational fluid dynamics (CFD) -- Wall shear stress (WSS) -- Helicity flow index (HFI) -- Marfan syndrome; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2015.11.040
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Implantation of a personalised external aortic root support (PEARS) in the Marfan aorta is a new procedure that has emerged recently, but its haemodynamic implication has not been investigated. The objective of this study was to compare the flow characteristics and hemodynamic indices in the aorta before and after insertion of PEARS, using combined cardiovascular magnetic resonance imaging (CMR) and computational fluid dynamics (CFD). Pre- and post-PEARS MR images were acquired from 3 patients and used to build patient-specific models and upstream flow conditions, which were incorporated into the CFD simulations. The results revealed that while the qualitative patterns of the haemodynamics were similar before and after PEARS implantation, the post-PEARS aortas had slightly less disturbed flow at the sinuses, as a result of reduced diameters in the post-PEARS aortic roots. Quantitative differences were observed between the pre- and post-PEARS aortas, in that the mean values of helicity flow index (HFI) varied by −10%, 35% and 20% in post-PEARS aortas of Patients 1, 2 and 3, respectively, but all values were within the range reported for normal aortas. Comparisons with MR measured velocities in the descending aorta of Patient 2 demonstrated that the computational models were able to reproduce the important flow features observed in vivo.