Key result
A novel image-based parametric equation estimated the elastic modulus of vessel walls with a mean percentage error of 9.3% compared to reference values, improving upon an 89.6% deviation.
Why the study?
There is a growing demand for in silico models to meet clinical requests, where image-based methods play a crucial role in estimating vessel wall elasticity non-invasively.
Does a novel parametric equation accurately estimate the elastic modulus of vessel walls from imaging data in in silico models?
Does a novel parametric equation accurately estimate the elastic modulus of vessel walls from imaging data in in silico models?
Absolute Event Rate: 9.3% vs 89.6%
A novel parametric equation significantly improves the non-invasive estimation of vessel wall elasticity from imaging data, reducing the mean percentage error to 9.3%.
New equation enables non-invasive vessel elasticity estimation from imaging; leaves open clinical adoption pending prospective validation.
Background: In the context of a growing demand for the use of in silico models to meet clinical requests, image-based methods play a crucial role. In this study, we present a parametric equation able to estimate the elasticity of vessel walls, non-invasively and indirectly, from information uniquely retrievable from imaging. Methods: A custom equation was iteratively refined and tuned from the simulations of a wide range of different vessel models, leading to the definition of an indirect method able to estimate the elastic modulus E of a vessel wall. To test the effectiveness of the predictive capability to infer the E value, two models with increasing complexity were used: a U-shaped vessel and a patient-specific aorta. Results: The original formulation was demonstrated to deviate from the ground truth, with a difference of 89.6%. However, the adoption of our proposed equation was found to significantly increase the reliability of the estimated E value for a vessel wall, with a mean percentage error of 9.3% with respect to the reference values. Conclusion: This study provides a strong basis for the definition of a method able to estimate local mechanical information of vessels from data easily retrievable from imaging, thus potentially increasing the reliability of in silico cardiovascular models.
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Fanni et al. (2022) studied this question. Novel parametric equation for estimating vessel wall elasticity vs. Original formulation was evaluated on Mean percentage error of estimated elastic modulus E with respect to reference values. A novel image-based parametric equation estimated the elastic modulus of vessel walls with a mean percentage error of 9.3% compared to reference values, improving upon an 89.6% deviation.
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