A Comprehensive Numerical Workflow for the Image-Based Estimation of In-Vivo Vascular Wall Mechanical Properties

  • M. Fanni, Benigno (BioCardioLab - Fondazione CNR)
  • Pizzuto, Alessandra († Pediatric Cardiology Unit, Ospedale del Cuore, Fondazione CNR)
  • Santoro, Giuseppe (Pediatric Cardiology Unit, Ospedale del Cuore, Fondazione CNR)
  • Celi, Simona (BioCardioLab - Fondazione CNR)

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In the last decade, the interest in patient-specific numerical modeling has kept spreading in the context of the decision-making process of cardiovascular interventions. However, the translation of such models into clinical practice still requires several gaps to be covered, as the adaption of cardiovascular models toward reliable patient-specific in-vivo conditions represents a major challenge [1]. Among the several limitations, the implementation in numerical models of in-vivo patient-specific mechanical properties represents at date the biggest source of uncertainty. The lack of such crucial information strongly limits the reliability of numerical simulation of patient-specific cardiovascular interventions, which depends not only on the accurate depiction of patient’s anatomy and reliable boundary conditions, but also on the mechanical interaction between the device and the implantation site. In this work, we developed a direct image-based methodology to infer the elastic module (E) of vessels in its specific in-vivo context, thus considering the surrounding tissue and neighborhood structures. Such method, namely χ-method, was refined from a previous study on the Pulse Wave Velocity (PWV) [2], generalizing its application on a wide range of vessels and allowing the estimation of patient-specific E value from solely in-vivo Phase Contrast Magnetic Resonance Imaging (PC MRI) data. The methodology consisted in a PWVbased formulation, which was iteratively refined using a series of numerical fluid-structure interaction simulations, which allowed to investigate a high number of vascular geometries and fluid dynamics scenarios in a fully controlled in-silico environment. In particular, each numerical vessel was treated as a virtual patient, from which velocity variation and area deformation along the simulated cardiac cycle were extracted at specific cross-sections, as obtainable from PC MRI. Such workflow allowed to define crucial parameters to be included in the PWV-based formulation to enhance the reliability of the E value estimation of vessels. After finalizing its mathematical formulation, the presented χ-method was tested on different virtual dataset generated from computer simulations with increased complexity. The application of the χ-method on the virtual PC MRI resulted in an average divergence from the reference E values of 9.3%, thus representing a promising tool to be applied on real patient cases.