Direct creation of patient-specific finite element models from medical images and preoperative prosthetic implant simulation using h-adaptive cartesian grids
- Autores: Luca Giovannelli
- Directores de la Tesis: Manuel Tur Valiente (dir. tes.), Juan José Ródenas García (dir. tes.)
- Lectura: En la Universitat Politècnica de València ( España ) en 2018
- Idioma: español
- Tribunal Calificador de la Tesis: A. Alberich Bayarri (presid.), María José Rupérez Moreno (secret.), Susanne Claus (voc.)
- Programa de doctorado: Programa de Doctorado en Ingeniería y Producción Industrial por la Universitat Politècnica de València
- Materias:
- Enlaces
- Tesis en acceso abierto en: RiuNet
- Resumen
In silico medicine is believed to be one of the most disruptive changes in the near future.
A great effort has been carried out during the last decade to develop predicting computational models to increase the diagnostic capabilities of medical doctors and the effectiveness of therapies. One of the key points of this revolution, will be personalisation, which means in most of the cases creating patient specific computational models, also called digital twins. This practice is currently wide-spread in research and there are quite a few software products in the market to obtain models from images. Nevertheless, in order to be usable in the clinical practice, these methods have to drastically reduce the time and human intervention required for the creation of the numerical models.
This thesis focuses on the proposal of image-based Cartesian grid Finite Element Method (cgFEM), a technique to automatically obtain numerical models from images and carry out linear structural analyses of bone, implants or heterogeneous materials.
In the method proposed in this thesis, after relating the image scale to corresponding elastic properties, all the pixel information will be used for the integration of the element stiffness matrices, which homogenise the elastic behaviour of the groups of pixels contained in each element. An initial uniform Cartesian mesh is h-adapted to the image characteristics by using an efficient refinement procedure which takes into account the local elastic properties associated to the pixel values. Doing so we avoid an excessive elastic property smoothing due to element integration in highly heterogeneous areas, but, nonetheless obtain final models with a reasonable number of degrees of freedom.
The result of the process is non-conforming mesh in which C0 continuity is enforced via multipoint constraints at the hanging nodes. In contrast to the standard procedures for the creation of Finite Element models from images, which usually require a complete and watertight definition of the geometry and treat the result as a standard CAD, with cgFEM it is not necessary to define any geometrical entity, as the procedure proposed leads to an implicit definition of the boundaries. Nonetheless, they are straightforward to include in the model if necessary, such as smooth surfaces to impose the boundary conditions more precisely or CAD device volumes for the simulation of implants. As a consequence, the amount of human work required for the creation of the numerical models is drastically reduced.
In this thesis, we analyse in detail the new method behaviour in 2D and 3D problems from CT-scans and X-ray images and synthetic images, focusing on three classes of problems. These include the simulation of bones, the material characterisation of solid foams from CT scans, for which we developed the cgFEM virtual characterisation technique, and the structural analysis of future implants, taking advantage of the capability of cgFEM to easily mix images and CAD models.
