An image-based approach for the estimation of arterial local stiffness in vivo

Guiding patient-specific cardiac simulations through data-assimilation of soft tissue kinematics from dynamic CT scan

Fluid-structure interaction (FSI) can be key in the generation of accurate digital replica of cardiovascular systems. To personalize these models, however, several patient-specific parameters need to be measured, which can be challenging to accomplish in a non-invasive manner. Alternatively, the cardiac kinematics of the patient can be extracted from imaging data and then directly imposed as a dynamic boundary condition in the computational model, also incorporating temporal and spatial measurement errors. A more advanced method combines FSI with kinematic driven simulations using data-assimilation. Despite its potential, the application of this technique to complex multi-physics cardiovascular simulations remains limited. In this study, we develop an FSI model of a patient's left ventricle (LV) and aorta, personalized with dynamic imaging data using a Nudging algorithm-a data assimilation technique-which is tailored to each cardiac chamber. In particular, for the LV, which embeds small-scale and irregular endocardial structures (higher measurement errors), the active contraction of the patient is replicated primarily using integral measurements (ventricular volume and surface area). On the other hand, the passive motion of the aorta is guided in the simulation relying directly on the local tissue positions from CT scan. The algorithm's simplicity and zero additional computational cost make it particularly suitable for multi-physics problems. Our results show that the assimilation procedure must be tuned to guide the system toward the measurements within the uncertainty range of the in-vivo data.

Case Report: Role of numerical simulations in the management of acute aortic syndromes

Penetrating aortic ulcer (PAU) represents a subset of acute aortic syndromes characterized by high rupture risk and management challenges, particularly in elderly patients with significant comorbidities. This case report showcases a 75-year-old patient with a history of coronary artery bypass graft (CABG) and with multiple PAUs involving the aortic arch, deemed unfit for conventional open surgery. A branched aortic endograft with a pre-cannulated side component for the left subclavian artery (LSA) was employed to preserve the patency of the previous CABG. Two computational fluid dynamics (CFD) simulations and a morphological analysis were performed on the pre- and post-intervention aortic configurations to evaluate changes in flow rate and pressure drop at LSA level and differences in the lumen size. The results revealed a decrease in the flow rate equal to 2.38% after the intervention and an increase in pressure drop of 4.48 mmHg, while the maximum differences in LSA cross-sectional areas and diameters were 1.49 cm2 and 0.64 cm, respectively. Minimal alteration in LSA blood flow due to the chosen intervention approach confirmed the effectiveness of the selected unibody design endograft with LSA preservation, ensuring myocardial perfusion. Therefore, CFD simulations demonstrate to be a powerful tool to evaluate the hemodynamic consequences of interventions by accurately estimating the main fluid dynamic parameters.

Numerical investigation on circular and elliptical bulge tests for inverse soft tissue characterization

The acquisition of insights concerning the mechanobiology of aneurysmatic aortic tissues is an important field of investigation. The complete characterization of aneurysm mechanical behaviour can be carried out by biaxial experimental tests on ex vivo specimens. In literature, several works proposed bulge inflation tests as a valid method to analyse aneurysmatic tissue. Bulge test data processing requires the adoption of digital image correlation and inverse analysis approaches to estimate strain and stress distributions, respectively. In this context, however, the accuracy of inverse analysis method has not been evaluated yet. This aspect appears particularly interesting given the anisotropic behaviour of the soft tissue and the possibility to adopt different die geometries. The goal of this study is to provide an accuracy characterization of the inverse analysis applied to the bulge test technique using a numerical approach. In particular, different cases of bulge inflation were simulated in a finite element environment as a reference. To investigate the effect of tissue anisotropic degree and bulge die geometries (circular and elliptical), different input parameters were considered to obtain multiple test cases. The specimen deformed shapes, resulting from the reference finite element simulations, were then analysed through an inverse analysis approach to produce an estimation of stress distributions. The estimated stresses were, at last, compared with the values from the reference finite element simulations. The results demonstrated that the circular die geometry produces a satisfactory estimation accuracy only under certain conditions of material quasi-isotropy. On the other hand, the choice of an elliptical bulge die was proven to be more suitable for the analysis of anisotropic tissues.

Enabling supra-aortic vessels inclusion in statistical shape models of the aorta: a novel non-rigid registration method

Statistical Shape Models (SSMs) are well-established tools for assessing the variability of 3D geometry and for broadening a limited set of shapes. They are widely used in medical imaging due to their ability to model complex geometries and their high efficiency as generative models. The principal step behind these techniques is a registration phase, which, in the case of complex geometries, can be a critical issue due to the correspondence problem, as it necessitates the development of correspondence mapping between shapes. The thoracic aorta, with its high level of morphological complexity, poses a multi-scale deformation problem due to the presence of several branch vessels with varying diameters. Moreover, branch vessels exhibit significant variability in shape, making the correspondence optimization even more challenging. Consequently, existing studies have focused on developing SSMs based only on the main body of the aorta, excluding the supra-aortic vessels from the analysis. In this work, we present a novel non-rigid registration algorithm based on optimizing a differentiable distance function through a modified gradient descent approach. This strategy enables the inclusion of custom, domain-specific constraints in the objective function, which act as landmarks during the registration phase. The algorithm's registration performance was tested and compared to an alternative Statistical Shape modeling framework, and subsequently used for the development of a comprehensive SSM of the thoracic aorta, including the supra-aortic vessels. The developed SSM was further evaluated against the alternative framework in terms of generalisation, specificity, and compactness to assess its effectiveness.

Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls

Introduction: Patient-specific computational models are a powerful tool for planning cardiovascular interventions. However, the in vivo patient-specific mechanical properties of vessels represent a major source of uncertainty. In this study, we investigated the effect of uncertainty in the elastic module (E) on a Fluid–Structure Interaction (FSI) model of a patient-specific aorta. Methods: The image-based χ-method was used to compute the initial E value of the vascular wall. The uncertainty quantification was carried out using the generalized Polynomial Chaos (gPC) expansion technique. The stochastic analysis was based on four deterministic simulations considering four quadrature points. A deviation of about ±20% on the estimation of the E value was assumed. Results: The influence of the uncertain E parameter was evaluated along the cardiac cycle on area and flow variations extracted from five cross-sections of the aortic FSI model. Results of stochastic analysis showed the impact of E in the ascending aorta while an insignificant effect was observed in the descending tract. Conclusions: This study demonstrated the importance of the image-based methodology for inferring E, highlighting the feasibility of retrieving useful additional data and enhancing the reliability of in silico models in clinical practice.