Using averaged models from 4D ultrasound strain imaging allows to signifcantly diferentiate local wall strains in calcifed regions of abdominal aortic aneurysms

Abdominal aortic aneurysms are a degenerative disease of the aorta associated with high mortality. To date, in vivo information to characterize the individual elastic properties of the aneurysm wall in terms of rupture risk is lacking. We have used time-resolved 3D ultrasound strain imaging to calculate spatially resolved in-plane strain distributions characterized by mean and local maximum strains, as well as indices of local variations in strains. Likewise, we here present a method to generate averaged models from multiple segmentations. Strains were then calculated for single segmentations and averaged models. After registration with aneurysm geometries based on CT-A imaging, local strains were divided into two groups with and without calcifications and compared. Geometry comparison from both imaging modalities showed good agreement with a root mean squared error of 1.22 ± 0.15 mm and Hausdorff Distance of 5.45 ± 1.56 mm (mean ± sd, respectively). Using averaged models, circumferential strains in areas with calcifications were 23.2 ± 11.7% (mean ± sd) smaller and significantly distinguishable at the 5% level from areas without calcifications. For single segmentations, this was possible only in 50% of cases. The areas without calcifications showed greater heterogeneity, larger maximum strains, and smaller strain ratios when computed by use of the averaged models. Using these averaged models, reliable conclusions can be made about the local elastic properties of individual aneurysm (and long-term observations of their change), rather than just group comparisons. This is an important prerequisite for clinical application and provides qualitatively new information about the change of an abdominal aortic aneurysm in the course of disease progression compared to the diameter criterion

A Proof of Concept of a Non-Invasive Image-Based Material Characterization Method for Enhanced Patient-Specific Computational Modeling

PURPOSE: Computational models of cardiovascular structures rely on their accurate mechanical characterization. A validated method able to infer the material properties of patient-specific large vessels is currently lacking. The aim of the present study is to present a technique starting from the flow-area (QA) method to retrieve basic material properties from magnetic resonance (MR) imaging. METHODS: The proposed method was developed and tested, first, in silico and then in vitro. In silico, fluid-structure interaction (FSI) simulations of flow within a deformable pipe were run with varying elastic modules (E) between 0.5 and 32 MPa. The proposed QA-based formulation was assessed and modified based on the FSI results to retrieve E values. In vitro, a compliant phantom connected to a mock circulatory system was tested within MR scanning. Images of the phantom were acquired and post-processed according to the modified formulation to infer E of the phantom. Results of in vitro imaging assessment were verified against standard tensile test. RESULTS: In silico results from FSI simulations were used to derive the correction factor to the original formulation based on the geometrical and material characteristics. In vitro, the modified QA-based equation estimated an average E = 0.51 MPa, 2% different from the E derived from tensile tests (i.e. E = 0.50 MPa). CONCLUSION: This study presented promising results of an indirect and non-invasive method to establish elastic properties from solely MR images data, suggesting a potential image-based mechanical characterization of large blood vessels

Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium

A long-standing problem at the frontier of biomechanical studies is to develop fast methods capable of estimating material properties from clinical data. In this paper, we have studied three surrogate models based on machine learning (ML) methods for fast parameter estimation of left ventricular (LV) myocardium. We use three ML methods named K-nearest neighbour (KNN), XGBoost and multi-layer perceptron (MLP) to emulate the relationships between pressure and volume strains during the diastolic filling. Firstly, to train the surrogate models, a forward finite-element simulator of LV diastolic filling is used. Then the training data are projected in a low-dimensional parametrized space. Next, three ML models are trained to learn the relationships of pressure–volume and pressure–strain. Finally, an inverse parameter estimation problem is formulated by using those trained surrogate models. Our results show that the three ML models can learn the relationships of pressure–volume and pressure–strain very well, and the parameter inference using the surrogate models can be carried out in minutes. Estimated parameters from both the XGBoost and MLP models have much less uncertainties compared with the KNN model. Our results further suggest that the XGBoost model is better for predicting the LV diastolic dynamics and estimating passive parameters than other two surrogate models. Further studies are warranted to investigate how XGBoost can be used for emulating cardiac pump function in a multi-physics and multi-scale framework