Quantification of Particle Residence Time in Abdominal Aortic Aneurysms Using Magnetic Resonance Imaging and Computational Fluid Dynamics
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Hemodynamic conditions are hypothesized to affect the initiation, growth, and rupture of abdominal aortic aneurysms (AAAs), a vascular disease characterized by progressive wall degradation and enlargement of the abdominal aorta. This study aims to use magnetic resonance imaging (MRI) and computational fluid dynamics (CFD) to quantify flow stagnation and recirculation in eight AAAs by computing particle residence time (PRT). Specifically, we used gadolinium-enhanced MR angiography to obtain images of the vessel lumens, which were used to generate subject-specific models. We also used phase-contrast MRI to measure blood flow at supraceliac and infrarenal locations to prescribe physiologic boundary conditions. CFD was used to simulate pulsatile flow, and PRT, particle residence index, and particle half-life of PRT in the aneurysms were computed. We observed significant regional differences of PRT in the aneurysms with localized patterns that differed depending on aneurysm geometry and infrarenal flow. A bulbous aneurysm with the lowest mean infrarenal flow demonstrated the slowest particle clearance. In addition, improvements in particle clearance were observed with increase of mean infrarenal flow. We postulate that augmentation of mean infrarenal flow during exercise may reduce chronic flow stasis that may influence mural thrombus burden, degradation of the vessel wall, and aneurysm growth.
KeywordsHemodynamics Pulsatile flow simulation Particle clearance Flow stagnation Flow waveforms Aneurysm geometry Subject-specific
Abdominal aortic aneurysm
Anterior to posterior
Left to right
Magnetic resonance imaging
Particle residence index
Particle residence time
Resistance (proximal)–Capacitance–Resistance (distal)
This research was supported by the National Institutes of Health (P50 HL083800, P41 RR09784), the Lucas Center for Magnetic Resonance Imaging, and NSF (CNS-0619926) for computer resources. Allen Chiou, Victoria Yeh, Yash Narang, and Bartlomiej R. Imielski provided assistance with imaging and modeling. Nan Xiao provided help with quantification of PRT data. We thank all research subjects for their participation.
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