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

Abstract

The analysis of mechanobiology of arterial tissues remains an important topic of research for cardiovascular pathologies evaluation. In the current state of the art, the gold standard to characterize the tissue mechanical behavior is represented by experimental tests, requiring the harvesting of ex-vivo specimens. In recent years though, image-based techniques for the in vivo estimation of arterial tissue stiffness were presented. The aim of this study is to define a new approach to provide local distribution of arterial stiffness, estimated as the linearized Young’s Modulus, based on the knowledge of in vivo patient-specific imaging data. In particular, the strain and stress are estimated with sectional contour length ratios and a Laplace hypothesis/inverse engineering approach, respectively, and then used to calculate the Young’s Modulus. After describing the method, this was validated by using a set of Finite Element simulations as input. In particular, idealized cylinder and elbow shapes plus a single patient-specific geometry were simulated. Different stiffness distributions were tested for the simulated patient-specific case. After the validation from Finite Element data, the method was then applied to patient-specific ECG-gated Computed Tomography data by also introducing a mesh morphing approach to map the aortic surface along the cardiac phases. The validation process revealed satisfactory results. In the simulated patient-specific case, root mean square percentage errors below 10% for the homogeneous distribution and below 20% for proximal/distal distribution of stiffness. The method was then successfully used on the three ECG-gated patient-specific cases. The resulting distributions of stiffness exhibited significant heterogeneity, nevertheless the resulting Young’s moduli were always contained within the 1–3 MPa range, which is in line with literature.

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

“…Numerical simulations have proven to be effective in analysing the aorta for both wall stress behaviour and fluid dynamics patterns ( 6 , 7 ). In particular, computational fluid dynamics (CFD) simulations are widely adopted to investigate fluid dynamics of healthy and pathological aorta ( 8 , 9 ), as well as to analyse the effects of medical devices and to predict potential negative outcomes of specific treatment procedure ( 10 , 11 ).…”

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

“…However, on the other hand, the mechanical behavior of in vivo vessels is the main source of uncertainty. The response of the vascular wall to blood flow [ 19 , 20 , 21 ] or device interaction [ 8 , 22 ] is not only contingent on the intrinsic material properties of the vessel, but it is also related to the properties of the surrounding structures and tissues [ 23 ]. In this context, several studies have focused on the extraction of in vivo mechanical properties, mainly coupling inverse computational techniques and imaging [ 24 , 25 , 26 ].…”

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

“…One example is during aortic aneurysm prognosis, for which the maximum diameter measurement is the most common 2d biomarker used ( Erbel et al, 2014 ). However, it is widely thought that this method has low prognostic significance since it fails to capture the full three-dimensional geometric variability that allows a higher comprehension of the disease ( Celi and Berti, 2014 ; Celi et al, 2023 ). Moreover, the maximum diameter does not exploit the abundance of information contained in the current tomographic imaging techniques available in clinics, e.g., computed tomography (CT) and magnetic resonance (MR) ( Celi et al, 2017 ).…”