Aortic coarctation (CoA) accounting for 3-11% of congenital heart disease can be successfully treated. Long-term results, however, have revealed decreased life expectancy associated with abnormal hemodynamics. Accordingly, an assessment of hemodynamics is the key factor in treatment decisions and successful long-term results. In this study, 3D angiography whole heart (3DWH) and 4D phase-contrast magnetic resonance imaging (MRI) data were acquired. Geometries of the thoracic aorta with CoAs were reconstructed using ZIB-Amira software. X-ray angiograms were used to evaluate the post-treatment geometry. Computational fluid dynamics models in three patients were created to simulate pre- and post-treatment situations using the FLUENT program. The aim of the study was to investigate the impact of the inlet velocity profile (plug vs. MRI-based) with a focus on the peak systole pressure gradient and wall shear stress (WSS). Results show that helical flow at the aorta inlet can significantly affect the assessment of pressure drop and WSS. Simplified plug inlet velocity profiles significantly (p < 0.05) overestimate the pressure drop in pre- and post-treatment geometries and significantly (p < 0.05) underestimate surface-averaged WSS. We conclude that the use of the physiologically correct but time-expensive 4D MRI-based in vivo velocity profile in CFD studies may be an important step towards a patient-specific analysis of CoA hemodynamics.

Hence, blood trauma might be reduced with this design. Based on these encouraging results, future in vitro investigations to investigate actual blood damage are intended.

A novel approach to calculate blood damage without using RSS as a damaging parameter is established. The results of our numerical experiment support the hypothesis that the use of RSS as a damaging parameter should be avoided.

Aortic coarctation (CoA) accounts for approximately 10% of congenital heart diseases 1. CoA causing high pressure gradient can be successfully treated surgical or catheter-based. Long-term results, however, revealed decreased life expectancy associated with abnormal hemodynamics 1. 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.