A new method for scaling inlet flow waveform in hemodynamic analysis of aortic dissection

Computational fluid dynamics (CFD) simulations have shown great potentials in cardiovascular disease diagnosis and postoperative assessment. Patient-specific and well-tuned boundary conditions are key to obtaining accurate and reliable hemodynamic results. However, CFD simulations are usually performed under non-patient-specific flow conditions due to the absence of in vivo flow and pressure measurements. This study proposes a new method to overcome this challenge by tuning inlet boundary conditions using data extracted from electrocardiogram (ECG). Five patient-specific geometric models of type B aortic dissection were reconstructed from computed tomography (CT) images. Other available data included stoke volume (SV), ECG, and 4D-flow magnetic resonance imaging (MRI). ECG waveforms were processed to extract patient-specific systole to diastole ratio (SDR). Inlet boundary conditions were defined based on a generic aortic flow waveform tuned using (1) SV only, and (2) with ECG and SV (ECG + SV). 4D-flow MRI derived inlet boundary conditions were also used in patient-specific simulations to provide the gold standard for comparison and validation. Simulations using inlet flow waveform tuned with ECG + SV not only successfully reproduced flow distributions in the descending aorta but also provided accurate prediction of time-averaged wall shear stress (TAWSS) in the primary entry tear (PET) and abdominal regions, as well as maximum pressure difference, ∆P