An integrated in-vitro and in-silico workflow to study the pulmonary bifurcation hemodynamics - Fanni, Benigno Marco; Gasparotti, Emanuele; Vignali, Emanuele; Capelli, Claudio; Positano, Vincenzo; Celi, Simona (Computers & fluids, 2023)
Tetralogy of Fallot (ToF) is the most common congenital heart disease. In this work, hemodynamics of pulmonary arteries (PA) in ToF patients was investigated using patient-specific image-based models. The study included the investigation of right and left pulmonary arteries (RPA and LPA, respectively). Numerical and experimental tools were used to reliably model the patient-specific fluid dynamics, including the pathological features present in ToF cases, i.e. regurgitation, hypertensive pressure and asymmetric right/left flow split. A genetic algorithm (GA) was developed to estimate two sets of patient-specific RCR values, according to the RPA–LPA flow split and pressure range of the subject. GA outcomes were validated using experimental and computational fluid dynamics (CFD) simulations. A mock loop circuit was set up including a 3D printed phantom of a representative pathological PA. The experiment was replicated with a CFD simulation. Both in-vitro and in-silico results showed very good agreement, with relative errors below 10%, with the prediction of the GA, demonstrating that the computed RCR values were suitable to provide the correct flow split and pressure range in ToF patients. In this study we present an image-based method with a custom GA algorithm for the estimation of patient-specific RCR parameters for a ToF PA bifurcation, taking into account the patient’s flow split of the subject. This approach is promising for further modeling of PA pathologies thus facilitating the translation of patient-specific simulations in clinics. [Display omitted] •An image-based pipeline to model the pulmonary bifurcation hemodynamics is proposed.•A custom genetic algorithm computes the lumped parameters from patient data.•Results of in-vitro and in-silico models match the prediction of genetic algorithm.•The feasibility to model in-vivo conditions of congenital patients is demonstrated.