We present a multi-scale CFD-based study conducted in a cohort of 11 patients with coarctation of the aorta (CoA). The study explores the potential for implementation of a workflow using non-invasive routinely collected medical imaging data and clinical measurements to provide a more detailed insight into local aortic haemodynamics in order to support clinical decision making. Our approach is multi-scale, using a reduced-order model (1D/0D) and an optimization process for the personalization of patient-specific boundary conditions and aortic vessel wall parameters from non-invasive measurements, to inform a more complex model (3D/0D) representing 3D aortic patient-specific anatomy. The reliability of the modelling approach is investigated by comparing 3D/0D model pressure drop estimation with measured peak gradients recorded during diagnostic cardiac catheterization and 2D PC-MRI flow rate measurements in the descending aorta. The current study demonstrated that the proposed approach requires low levels of user interaction, making it suitable for the clinical setting. The agreement between computed blood pressure drop and catheter measurements is 10 ± 8 mmHg at the coarctation site. The comparison between CFD derived and catheter measured pressure gradients indicated that the model has to be improved, suggesting the use of time varying pressure waveforms to further optimize the tuning process and modelling assumptions.
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