Patient-Specific Multiscale Modeling of Blood Flow for Coronary Artery Bypass Graft Surgery
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We present a computational framework for multiscale modeling and simulation of blood flow in coronary artery bypass graft (CABG) patients. Using this framework, only CT and non-invasive clinical measurements are required without the need to assume pressure and/or flow waveforms in the coronaries and we can capture global circulatory dynamics. We demonstrate this methodology in a case study of a patient with multiple CABGs. A patient-specific model of the blood vessels is constructed from CT image data to include the aorta, aortic branch vessels (brachiocephalic artery and carotids), the coronary arteries and multiple bypass grafts. The rest of the circulatory system is modeled using a lumped parameter network (LPN) 0 dimensional (0D) system comprised of resistances, capacitors (compliance), inductors (inertance), elastance and diodes (valves) that are tuned to match patient-specific clinical data. A finite element solver is used to compute blood flow and pressure in the 3D (3 dimensional) model, and this solver is implicitly coupled to the 0D LPN code at all inlets and outlets. By systematically parameterizing the graft geometry, we evaluate the influence of graft shape on the local hemodynamics, and global circulatory dynamics. Virtual manipulation of graft geometry is automated using Bezier splines and control points along the pathlines. Using this framework, we quantify wall shear stress, wall shear stress gradients and oscillatory shear index for different surgical geometries. We also compare pressures, flow rates and ventricular pressure–volume loops pre- and post-bypass graft surgery. We observe that PV loops do not change significantly after CABG but that both coronary perfusion and local hemodynamic parameters near the anastomosis region change substantially. Implications for future patient-specific optimization of CABG are discussed.
KeywordsHemodynamics Bypass graft Wall shear stress Coronary artery Multiscale modeling
The authors acknowledge funding from an American Heart Association postdoctoral fellowship, the Leducq Foundation, a Burroughs Wellcome Fund Career Award at the Scientific Interface, and NIH grant RHL102596A for funding this work. We thank Hyun Jin Kim for expertise on coronary modeling, and Francesco Migliavacca for expertise on multiscale modeling. Dr. Guccione acknowledges funding from the National Institute of Health, grant numbers R01HL077921 and R01HL086400.
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