Need For A More Representative Constitutive Model For Severely Calcified Aortic Valves
Asad M. Mirza, Sharan Ramaswamy.
Florida International University, Miami, FL, USA.
OBJECTIVE:Existing simulations for calcified aortic valves (CAVs) assume valve tissue and calcifications are separate. However, actual calcified tissue is made of interconnected materials. Here, we investigate severely calcified aortic valve (SCAV) simulations using commonly structural properties and determine the level of agreement between them to ascertain if a more robust CAV model is required.
METHODS:A CAD model of a SCAV from an 82-year-old female patient at the early-diastolic phase was acquired commercially (Valve-012-Heart Print catalog, Materialise., Plymouth MI). Aortic valve components were assumed isotropic linear elastic, E = 20 MPa and v = 0.45. For the calcifications three models were tested: an isotropic linear elastic model, E = 20 MPa, a hyper-elastic 1st order Ogden model, μ = 13.3 kPa and α = 24.88, and a hyper-elastic 5 parameter Mooney-Rivlin model, with density set to 1600 kg/m3 and v = 0.45. Valve leaflets was prescribed with 120 mmHg on the ventricularis side. Ventricular inlet and aortic outflow ends were fixed for space and rotation.
RESULTS:Linear elastic, Ogden, and 5 parameter Mooney-Rivlin models had a geometric orifice area (GOA) of 0.210 cm2, 0.309 cm2, and 0.267 cm2, respectively, during peak systole. There is qualitatively (Fig. 1) little agreement on the final peak systolic configuration between any of the constitutive models. Particularly, there is a greater difference between the linear elastic model (Fig. 1A) compared to its hyper-elastic counterparts (Fig. 1B & 1C).
CONCLUSIONS:Current CAV models cannot fully capture valve dynamics, nor can they agree on clinical geometric quantities of interest such as GOA, whose accuracy is critical in order for these computational models to be able to guide subsequent clinical interventions. Thus, there exists a need for a more representative constitutive model for CAVs that is both clinically accurate and computationally robust.
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