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contributor authorTajeddini, Farshad
contributor authorRomero, David A.
contributor authorMcClarty, Davis
contributor authorChung, Jennifer
contributor authorAmon, Cristina H.
date accessioned2023-08-16T18:17:44Z
date available2023-08-16T18:17:44Z
date copyright3/24/2023 12:00:00 AM
date issued2023
identifier issn0098-2202
identifier otherfe_145_06_061106.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4291782
description abstractComputational fluid dynamics (CFD) has been widely used to predict and understand cardiovascular flows. However, the accuracy of CFD predictions depends on faithful reconstruction of patient vascular anatomy and accurate patient-specific inlet and outlet boundary conditions. 4-Dimensional magnetic resonance imaging (4D MRI) can provide patient-specific data to obtain the required geometry and time-dependent flow boundary conditions for CFD simulations, and can further be used to validate CFD predictions. This work presents a framework to combine both spatiotemporal 4D MRI data and patient monitoring data with CFD simulation workflows. To assist practitioners, all aspects of the modeling workflow, from geometry reconstruction to results postprocessing, are illustrated and compared using three software packages (ansys, comsol, SimVascular) to predict hemodynamics in the thoracic aorta. A sensitivity analysis with respect to inlet boundary condition is presented. Results highlight the importance of 4D MRI data for improving the accuracy of flow predictions on the ascending aorta and the aortic arch. In contrast, simulation results for the descending aorta are less sensitive to the patient-specific inlet boundary conditions.
publisherThe American Society of Mechanical Engineers (ASME)
titleWorkflow Comparison for Combined 4D MRI/CFD Patient-Specific Cardiovascular Flow Simulations of the Thoracic Aorta
typeJournal Paper
journal volume145
journal issue6
journal titleJournal of Fluids Engineering
identifier doi10.1115/1.4057047
journal fristpage61106-1
journal lastpage61106-11
page11
treeJournal of Fluids Engineering:;2023:;volume( 145 ):;issue: 006
contenttypeFulltext


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