Synthetic Database of Aortic Morphometry and Hemodynamics: Overcoming Medical Imaging Data Availability.

Modeling of hemodynamics and artificial intelligence have great potential to support clinical diagnosis and decision making. While hemodynamics modeling is extremely time- and resource-consuming, machine learning (ML) typically requires large training data that are often unavailable. The aim of this study was to develop and evaluate a novel methodology generating a large database of synthetic cases with characteristics similar to clinical cohorts of patients with coarctation of the aorta (CoA), a congenital heart disease associated with abnormal hemodynamics. Synthetic data allows use of ML approaches to investigate aortic morphometric pathology and its influence on hemodynamics. Magnetic resonance imaging data (154 patients as well as of healthy subjects) of aortic shape and flow were used to statistically characterize the clinical cohort. The methodology generating the synthetic cohort combined statistical shape modeling of aortic morphometry and aorta inlet flow fields and numerical flow simulations. Hierarchical clustering and non-linear regression analysis were successfully used to investigate the relationship between morphometry and hemodynamics and to demonstrate credibility of the synthetic cohort by comparison with a clinical cohort. A database of 2652 synthetic cases with realistic shape and hemodynamic properties was generated. Three shape clusters and respective differences in hemodynamics were identified. The novel model predicts the CoA pressure gradient with a root mean square error of 4.6 mmHg. In conclusion, synthetic data for anatomy and hemodynamics is a suitable means to address the lack of large datasets and provide a powerful basis for ML to gain new insights into cardiovascular diseases.

Blood Pump Design Variations and Their Influence on Hydraulic Performance and Indicators of Hemocompatibility.

Patients with ventricular assist devices still suffer from high rates of adverse events. Since many of these complications are linked to the flow field within the pump, optimization of the device geometry is essential. To investigate design aspects that influence the flow field, we developed a centrifugal blood pump using industrial guidelines. We then systematically varied selected design parameters and investigated their effects on hemodynamics and hydraulic performance using computational fluid dynamics. We analysed the flow fields based on Eulerian and Lagrangian features, shear stress histograms and six indicators of hemocompatibility. Within the investigated range of clearance gaps (50-500 µm), number of impeller blades (4-7), and semi-open versus closed shroud design, we found association of potentially damaging shear stress conditions with larger gap size and more blades. The extent of stagnation and recirculation zones was reduced with lower numbers of blades and a semi-open impeller, but it was increased with smaller clearance. The Lagrangian hemolysis index, a metric commonly applied to estimate blood damage, showed a negative correlation with hydraulic efficiency and no correlation with the Eulerian threshold-based metric.

In vitro study of near-wall flow in a cerebral aneurysm model with and without coils.

BACKGROUND AND PURPOSE: Coil embolization procedures change the flow conditions in the cerebral aneurysm and, therefore, in the near-wall region. Knowledge of these flow changes may be helpful to optimize therapy. The goal of this study was to investigate the effect of the coil-packing attenuation on the near-wall flow and its variability due to differences in the coil structure. MATERIALS AND METHODS: An enlarged transparent model of an ACA aneurysm was fabricated on the basis of CT angiography. The near-wall flow was visualized by using a recently proposed technique called Wall-PIV. Coil-packing attenuation of 10%, 15%, and 20% were investigated and compared with an aneurysmal flow without coils. Then the flow variability due to the coil introduction was analyzed in 10 experiments by using a packing attenuation of 15%. RESULTS: A small packing attenuation of 10% already alters the near-wall flow significantly in a large part of the aneurysmal sac. These flow changes are characterized by a slow flow with short (interrupted) path lines. An increased packing attenuation expands the wall area exposed to the altered flow conditions. This area, however, depends on the coil position and/or on the 3D coil structure in the aneurysm. CONCLUSIONS: To our knowledge, this is the first time the near-wall flow changes caused by coils in an aneurysm model have been visualized. It can be concluded that future hydrodynamic studies of coil therapy should include an investigation of the coil structure in addition to the coil-packing attenuation.