Pavlo Yevtushenko

MRI-based computational fluid dynamics for diagnosis and treatment prediction: clinical validation study in patients with coarctation of aorta.

To reduce the need for diagnostic catheterization and optimize treatment in a variety of congenital heart diseases, magnetic resonance imaging (MRI)‐based computational fluid dynamics (CFD) is proposed. However, data about the accuracy of CFD in a clinical context are still sparse. To fill this gap, this study compares MRI‐based CFD to catheterization in the coarctation of aorta (CoA) setting.

Hemodynamic Evaluation of a Biological and Mechanical Aortic Valve Prosthesis Using Patient-Specific MRI-Based CFD: MRI-BASED CFD COMPARISON OF TWO AORTIC VALVE PROSTHESES

Modeling different treatment options before a procedure is performed is a promising approach for surgical decision making and patient care in heart valve disease. This study investigated the hemodynamic impact of different prostheses through patient-specific MRI-based CFD simulations. Ten time-resolved MRI data sets with and without velocity encoding were obtained to reconstruct the aorta and set hemodynamic boundary conditions for simulations. Aortic hemodynamics after virtual valve replacement with a biological and mechanical valve prosthesis were investigated. Wall shear stress (WSS), secondary flow degree (SFD), transvalvular pressure drop (TPD), turbulent kinetic energy (TKE), and normalized flow displacement (NFD) were evaluated to characterize valve-induced hemodynamics. The biological prostheses induced significantly higher WSS (medians: 9.3 vs. 8.6 Pa, P = 0.027) and SFD (means: 0.78 vs. 0.49, P = 0.002) in the ascending aorta, TPD (medians: 11.4 vs. 2.7 mm Hg, P = 0.002), TKE (means: 400 vs. 283 cm2 /s2 , P = 0.037), and NFD (means: 0.0994 vs. 0.0607, P = 0.020) than the mechanical prostheses. The differences between the prosthesis types showed great inter-patient variability, however. Given this variability, a patient-specific evaluation is warranted. In conclusion, MRI-based CFD offers an opportunity to assess the interactions between prosthesis and patient-specific boundary conditions, which may help in optimizing surgical decision making and providing additional guidance to clinicians.

Impact of patient-specific LVOT inflow profiles on aortic valve prosthesis and ascending aorta hemodynamics

Abstract Patient-specific models become increasingly important in cardiovascular research as they allow prediction of surgical procedures. While the left ventricular outflow profile is an essential boundary condition, it remains unknown before treatment takes place. To overcome this problem, hemodynamics after virtual valve replacement were calculated based on different inlet profiles at the left ventricular outflow tract: a generic plug profile and a profile derived from 4D-flow-MRI. Spatially averaged parameters within the aorta were not significantly altered using either profile. A generic profile might be sufficient for the prediction of hemodynamics, circumventing the problem of predicting change in patient-specific boundary conditions.

Model-Based Therapy Planning Allows Prediction of Haemodynamic Outcome after Aortic Valve Replacement

Optimizing treatment planning is essential for advances in patient care and outcomes. Precisely tailored therapy for each patient remains a yearned-for goal. Cardiovascular modelling has the potential to simulate and predict the functional response before the actual intervention is performed. The objective of this study was to proof the validity of model-based prediction of haemodynamic outcome after aortic valve replacement. In a prospective study design virtual (model-based) treatment of the valve and the surrounding vasculature were performed alongside the actual surgical procedure (control group). The resulting predictions of anatomic and haemodynamic outcome based on information from magnetic resonance imaging before the procedure were compared to post-operative imaging assessment of the surgical control group in ten patients. Predicted vs. post-operative peak velocities across the valve were comparable (2.97 ± 1.12 vs. 2.68 ± 0.67 m/s; p = 0.362). In wall shear stress (17.3 ± 12.3 Pa vs. 16.7 ± 16.84 Pa; p = 0.803) and secondary flow degree (0.44 ± 0.32 vs. 0.49 ± 0.23; p = 0.277) significant linear correlations (p < 0.001) were found between predicted and post-operative outcomes. Between groups blood flow patterns showed good agreement (helicity p = 0.852, vorticity p = 0.185, eccentricity p = 0.333). Model-based therapy planning is able to accurately predict post-operative haemodynamics after aortic valve replacement. These validated virtual treatment procedures open up promising opportunities for individually targeted interventions.

Numerical investigation of the impact of branching vessel boundary conditions on aortic hemodynamics

Abstract CFD has gained significant attention as a tool to model aortic hemodynamics. However, obtaining accurate patient-specific boundary conditions still poses a major challenge and represents a major source of uncertainties, which are difficult to quantify. This study presents an attempt to quantify these uncertainties by comparing 14 patient-specific simulations of the aorta (reference method), each exhibiting stenosis, against simulations using the same geometries without the branching vessels of the aortic arch (simplified method). Results were evaluated by comparing pressure drop along the aorta, secondary flow degree (SFD) and surface-averaged wall shear stress (WSS) for each patient. The comparison shows little difference in pressure drop between the two methods (simplified-reference) with the mean difference being 1.2 mmHg (standard deviation: 3.0 mmHg). SFD and WSS, however, show striking differences between the methods: SFD downstream of the stenosis is on average 61 % higher in the simplified cases, while WSS is on average 3.0 Pa lower in the simplified cases. Although unphysiological, the comparison of both methods gives an upper bound for the error introduced by uncertainties in branching vessel boundary conditions. For the pressure drop this error appears to be remarkably low, while being unacceptably high for SFD and WSS.

Hemodynamic in Aortic Coarctation Using MRI-Based Inflow Condition

Image-based CFD can support diagnosis, treatment decision and planning. The ability of CFD to calculate pressure drop across the aortic coarctation is the focus of the 2013 STACOM Challenge. The focus of our study was inflow conditions. We compared a MRI-based inlet velocity profile with a swirl and an often used plug velocity profile without swirl. The unsteady flow simulations were performed using the solver FLUENT with consideration of the challenge specifications. For outflows, the constant outflow ratios of the supra-aortic vessels were set. The consideration of a secondary flow swirl at the inlet of the ascending aorta significantly affect reduce the calculated pressure drop across the aortic coarctation and hence the treatment decision. Furthermore, using MRI-measured flow rates at the ascending and the descending aorta without a proof of data consistency could result in an overestimated pressure drop due to overestimated flow into the supra-aortic vessels.

Treatment of the Aortic Coarctation: Prediction of the Hemodynamic Impact

Aortic coarctation (CoA) accounts for approximately 10% of congenital heart diseases1. CoA causing high pressure gradient can be successfully treated surgical or catheter-based. Long-term results, however, revealed decreased life expectancy associated with abnormal hemodynamics1. To develop a next-generation personalized diagnostic-prognostic tools allowing treatment optimization and thus to improve life expectance, the innovative combination of imaging science, biofluid mechanics, and computer modeling is necessary. Patient-specific computational fluid dynamics (CFD) models of the CoA based on MRI data were created to analyze pre- and post-treatment hemodynamics with a focus on pressure gradient.Copyright