From 4D medical images (CT, MRI, and Ultrasound) to 4D structured mesh models of the left ventricular endocardium for patient-specific simulations

With cardiovascular disease (CVD) remaining the primary cause of death worldwide, early detection of CVDs becomes essential. The intracardiac flow is an important component of ventricular function, motion kinetics, wash-out of ventricular chambers, and ventricular energetics. Coupling between Computational Fluid Dynamics (CFD) simulations and medical images can play a fundamental role in terms of patient-specific diagnostic tools. From a technical perspective, CFD simulations with moving boundaries could easily lead to negative volumes errors and the sudden failure of the simulation. The generation of high-quality 4D meshes (3D in space + time) with 1-to-l vertex becomes essential to perform a CFD simulation with moving boundaries. In this context, we developed a semiautomatic morphing tool able to create 4D high-quality structured meshes starting from a segmented 4D dataset. To prove the versatility and efficiency, the method was tested on three different 4D datasets (Ultrasound, MRI, and CT) by evaluating the quality and accuracy of the resulting 4D meshes. Furthermore, an estimation of some physiological quantities is accomplished for the 4D CT reconstruction. Future research will aim at extending the region of interest, further automation of the meshing algorithm, and generating structured hexahedral mesh models both for the blood and myocardial volume

optimisation of industrial parts by mesh morphing enabled automatic shape sculpting

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A fast and robust patient specific Finite Element mesh registration technique: application to 60 clinical cases

Finite Element mesh generation remains an important issue for patient specific biomechanical modeling. While some techniques make automatic mesh generation possible, in most cases, manual mesh generation is preferred for better control over the sub-domain representation, element type, layout and refinement that it provides. Yet, this option is time consuming and not suited for intraoperative situations where model generation and computation time is critical. To overcome this problem we propose a fast and automatic mesh generation technique based on the elastic registration of a generic mesh to the specific target organ in conjunction with element regularity and quality correction. This Mesh-Match-and-Repair (MMRep) approach combines control over the mesh structure along with fast and robust meshing capabilities, even in situations where only partial organ geometry is available. The technique was successfully tested on a database of 5 pre-operatively acquired complete femora CT scans, 5 femoral heads partially digitized at intraoperative stage, and 50 CT volumes of patients' heads. The MMRep algorithm succeeded in all 60 cases, yielding for each patient a hex-dominant, Atlas based, Finite Element mesh with submillimetric surface representation accuracy, directly exploitable within a commercial FE software

automatic shape optimization of structural components with manufacturing constraints

Abstract Among optimization procedures, mesh morphing gained a relevant position: it proved to be a suitable tool in obtaining weight and stress concentration reduction, without the need to iterate the numerical model generation. Shape modification through mesh morphing can be performed in an automatic fashion adopting two approaches: defining parameters which will describe the modified shape or exploiting results coming from numerical analyses. With this second approach, it is possible to achieve a very high automation grade: stress values retrieved on component surfaces can be successfully employed to drive the shape modification of the component itself. This 'driven-by-numerical-results' automatic approach can lead to complex optimized shapes, which can be easily achieved with modern additive manufacturing processes, but not adopting traditional manufacturing processes. In the present work a method to include manufacturing constraints in a shape optimization workflow is presented and applied to different structural optimization cases, in order to demonstrate how even manufacturing based on traditional processes can take advantage of automatic shape optimization of structural components

MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability

When learning simulations for modeling physical phenomena in industrial designs, geometrical variabilities are of prime interest. While classical regression techniques prove effective for parameterized geometries, practical scenarios often involve the absence of shape parametrization during the inference stage, leaving us with only mesh discretizations as available data. Learning simulations from such mesh-based representations poses significant challenges, with recent advances relying heavily on deep graph neural networks to overcome the limitations of conventional machine learning approaches. Despite their promising results, graph neural networks exhibit certain drawbacks, including their dependency on extensive datasets and limitations in providing built-in predictive uncertainties or handling large meshes. In this work, we propose a machine learning method that do not rely on graph neural networks. Complex geometrical shapes and variations with fixed topology are dealt with using well-known mesh morphing onto a common support, combined with classical dimensionality reduction techniques and Gaussian processes. The proposed methodology can easily deal with large meshes without the need for explicit shape parameterization and provides crucial predictive uncertainties, which are essential for informed decision-making. In the considered numerical experiments, the proposed method is competitive with respect to existing graph neural networks, regarding training efficiency and accuracy of the predictions

Study of post-necking hardening identification and deformation-induced surface roughening of metals

This thesis covers topics at two fundamental scales at which plastic deformation is occurring – macroscopic and microscopic. The first topic is related to the localization of deformation and subsequent necking that happens in metals during deformation, e.g., sheet metal during forming. The open fundamental question is – what happens to the material properties inside of the localized region. The localization process is very challenging to analyze, even using a conventional tension test since several effects such as material hardening as well as effects of strain-rate and temperature are strongly coupled. In this thesis we propose a novel approach that allows to decouple these effects and furthermore to identify the true hardening behavior of material. The solution is based on solving an inverse problem that involves optimization of an expensive black-box function. The methodology developed is presented in detail. In the second topic we consider the microscopic aspects of deformation, namely grain-scale plasticity. More specifically, we apply a crystal plasticity finite element framework to analyze the deformation-induced surface roughening effect. This task also involves a number of challenges. One such challenge is the accurate calibration of the model, which was tackled here using the black-box optimization procedure developed earlier. The second challenge is the accurate non-destructive reconstruction of a 3D texture based on images of several planar sections. The texture reconstruction problem was solved and presented as a general methodology. Subsequently it was possible to construct a comprehensive model that accounts for all major effects. It is shown that this model is able to capture the physics of deformation-induced surface roughening, however primarily in the average sense