Calibration of the mechanical boundary conditions for a patient-specific thoracic aorta model including the heart motion effect

TL;DR

This study addresses the problem of high-fidelity, patient-specific modeling of the thoracic aorta by calibrating four boundary-condition parameters that capture viscoelastic external tissue support and heart-induced annulus motion. The authors integrate cine-MRI-derived geometry and annulus motion with a decoupled CFD pressure field and a structural Mooney–Rivlin wall model governed by Robin boundary conditions, followed by a Levenberg–Marquardt calibration loop to minimize image-model boundary distances. They demonstrate that calibration reduces registration errors (max distance from 8.64 mm to 6.37 mm; mean distance from 2.24 mm to 1.83 mm) and achieves a strong agreement between structural and fully coupled FSI simulations (RMSE up to 0.19 mm, $D_{MAX_{FSI}}=0.64$ mm), thereby enhancing the model’s fidelity to patient-specific kinematics. Although performed on a single patient, the workflow provides a computationally efficient path toward personalized risk assessment and could underpin future digital twin frameworks for cardiovascular disease management.

Abstract

Objective: we propose a procedure for calibrating 4 parameters governing the mechanical boundary conditions (BCs) of a thoracic aorta (TA) model derived from one patient with ascending aortic aneurysm. The BCs reproduce the visco-elastic structural support provided by the soft tissue and the spine and allow for the inclusion of the heart motion effect. Methods: we first segment the TA from magnetic resonance imaging (MRI) angiography and derive the heart motion by tracking the aortic annulus from cine-MRI. A rigid-wall fluid-dynamic simulation is performed to derive the time-varying wall pressure field. We build the finite element model considering patient-specific material properties and imposing the derived pressure field and the motion at the annulus boundary. The calibration, which involves the zero-pressure state computation, is based on purely structural simulations. After obtaining the vessel boundaries from the cine-MRI sequences, an iterative procedure is performed to minimize the distance between them and the corresponding boundaries derived from the deformed structural model. A strongly-coupled fluid-structure interaction (FSI) analysis is finally performed with the tuned parameters and compared to the purely structural simulation. Results and Conclusion: the calibration with structural simulations allows to reduce maximum and mean distances between image-derived and simulation-derived boundaries from 8.64 mm to 6.37 mm and from 2.24 mm to 1.83 mm, respectively. The maximum root mean square error between the deformed structural and FSI surface meshes is 0.19 mm. This procedure may prove crucial for increasing the model fidelity in replicating the real aortic root kinematics.

Figures (10)

• Figure 1: Workflow of the calibration procedure.
• Figure 2: The aortic model, the spine and the cine-MRI slice from which the area corresponding to the valve jet could be observed are shown. The fluid domain inlet was created on the specific plane by segmenting the area extracted from this cine-MRI acquisition during systole.
• Figure 3: a) The annulus plane tracked in a cine-MRI sequence. b) The motion ($DX(t), DY(t), DZ(t)$) derived from all landmarks.
• Figure 4: a) The CFD model of the aorta and the lumped parameters used to generate the boundary conditions. b) The structural model with the Robin BCs: the ascending tract on which the calibration is performed is shown in purple. c) The dummy node displacement.
• Figure 5: Splines for two given cine-MRI sequences in a specific frame.