Quantitative Assessment of Turbulence and Flow Eccentricity in an Aortic Coarctation - Impact of Virtual Interventions
2015 (English)In: Cardiovascular Engineering and Technology, ISSN 1869-408X, E-ISSN 1869-4098, Vol. 6, no 6, 281-293 p.Article in journal (Refereed) Published
Turbulence and flow eccentricity can be measured by magnetic resonance imaging (MRI) and may play an important role in the pathogenesis of numerous cardiovascular diseases. In the present study, we propose quantitative techniques to assess turbulent kinetic energy (TKE) and flow eccentricity that could assist in the evaluation and treatment of stenotic severities. These hemodynamic parameters were studied in a pre-treated aortic coarctation (CoA) and after several virtual interventions using computational fluid dynamics (CFD), to demonstrate the effect of different dilatation options on the flow field. Patient-specific geometry and flow conditions were derived from MRI data. The unsteady pulsatile flow was resolved by large eddy simulation (LES) including non-Newtonian blood rheology. Results showed an inverse asymptotic relationship between the total amount of TKE and degree of dilatation of the stenosis, where turbulent flow proximal the constriction limits the possible improvement by treating the CoA alone. Spatiotemporal maps of TKE and flow eccentricity could be linked to the characteristics of the jet, where improved flow conditions were favored by an eccentric dilatation of the CoA. By including these flow markers into a combined MRI-CFD intervention framework, CoA therapy has not only the possibility to produce predictions via simulation, but can also be validated pre- and immediate post treatment, as well as during follow-up studies.
Place, publisher, year, edition, pages
Springer, 2015. Vol. 6, no 6, 281-293 p.
Computational fluid dynamics, Large eddy simulation, Turbulent kinetic energy, Flow displacement, Non-Newtonian, Virtual treatment, Magnetic resonance imaging
IdentifiersURN: urn:nbn:se:liu:diva-114496DOI: 10.1007/s13239-015-0218-xISI: 000380356800007OAI: oai:DiVA.org:liu-114496DiVA: diva2:790422
Funding agencies: Swedish Research Council; Center for Industrial Information Technology (CENIIT); Swedish National Infrastructure for Computing (SNIC)2015-02-242015-02-242016-09-19Bibliographically approved