Inverse analysis of patient-specific parameters of a 3D-0D closed-loop cardiovascular model with an exemplary application to an adult tetralogy of Fallot case.

Abstract

Patient-specific computational models of the cardiovascular system can inform clinical decision-making by providing physics-based, non-invasive calculations of quantities that cannot be measured or are impractical to measure and by predicting physiological changes due to interventions. In particular, mixed-dimensional 3D-0D coupled models can represent spatially resolved 3D myocardial tissue mechanics and 0D pressure-flow relationships in heart valves and vascular system compartments, while accounting for their interactions in a closed-loop setting. We present an inverse analysis framework for the automated identification of a set of 3D and 0D patient-specific parameters based on flow, pressure, and cine cardiac MRI measurements. We propose a novel decomposition of the underlying large, nonlinear, and mixed-dimensional inverse problem into an equivalent set of independently solvable, computationally efficient, and well-posed inverse subproblems. This decomposition is enabled by the availability of measurement data of the coupling quantities and ensures a faster convergence toward a unique minimum. The inverse subproblems are solved with a L-BFGS optimization algorithm and an adjoint gradient evaluation. The proposed framework is demonstrated in a clinical case study of an adult repaired tetralogy of Fallot (ToF) patient with severe pulmonary regurgitation. The identified parameters provide a good agreement between measured and computed flows, pressures, and chamber volumes, ensuring a patient-specific model response. The outcome prediction of an in silico pulmonary valve replacement using the personalized model is physiologically consistent and correlates well with postoperative measurements. The proposed framework is essential for developing accurate and reliable cardiovascular digital twins and exploiting their predictive capabilities for intervention planning.