Biomedical Engineering Reference
In-Depth Information
Computational Fluid Dynamics
Framework for Large-Scale
Simulation in Pediatric Cardiology
Krist´f Ralovich, Razvan Ionasec, Viorel Mihalef, Puneet Sharma,
Bogdan Georgescu, Allen Everett, Nassir Navab, and Dorin Comaniciu
Abstract There is a high demand for patient specific cardiovascular therapeutics,
especially in pediatric cardiology which is confronted with complex and rather
unique congenital diseases. Current predictors for disease severity and treatment
selection have been proven to be suboptimal creating profound burden of premature
morbidity and mortality. Over the past decade, the influence of blood hemodynam-
ics has become increasingly acknowledged, especially in the context of congenital
diseases of the aortic arch. MRI-based 2D and 3D flow measurements are nowadays
possible, although restricted by cumbersome acquisition protocols and limited
acquisition resolution. Computational fluid dynamics (CFD) offers a valuable
alternative that also enables treatment outcome prediction. However, the current
methods rely on a sequence of complicated manual steps that lack the scalability
required within clinical settings. We propose a computation framework for large-
scale hemodynamics simulations in pediatric cardiology to aid diagnostic and
therapy decision making in patients affected by congenital disease of the aortic
valve (AV) and the aorta. Our method provides a deterministic and streamlined
processing pipeline to perform CFD simulations based on patient-specific boundary
conditions. Thus, blood flow simulations are performed using an embedded bound-
ary method within a level-set formulation with boundary conditions provided by
patient-specific anatomical and hemodynamical models. The capabilities of our
framework are demonstrated by performing blood flow analysis on patients selected
from an FDA-sponsored multicenter clinical trial.
K. Ralovich (
*
) • N. Navab
Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany
e-mail:
ralovich@in.tum.de
R. Ionasec • V. Mihalef • P. Sharma • B. Georgescu • D. Comaniciu
Siemens Corporate Research, Princeton, NJ 08540, USA
A. Everett
Johns Hopkins University, Baltimore, MD 21287, USA
