Generation of computational meshes from MRI and CT-scan data

Abstract

Abstract. There are fields of engineering where accurate and personalised data are required; biomedical applications are one such example. We describe here a general purpose method to create computational meshes based on the analysis and segmentation of raw medical imaging data. The various ingredients are not new: a segmentation method based on deformable contours and a surface and volume mesh adaptation method based on discrete metric specifications; but the challenge that motivated this paper is to put them together in an attempt to design an automatic, easy to use and efficient 3D code.For non-engineering (like biomedical) applications, the user interface is often a key point. In this project, we put a great deal of emphasis on the automation of the whole procedure, making then possible to envisage large scale simulations with a minimal amount of user interaction. In particular, the user knowledge is required to help segmenting the image (the user is expected to have the knowhow of the body anatomy), all meshing steps rely on fully automatic algorithms depending on a few parameters (for the external surface approximation).One application example is presented and commented, for which the data preparation takes a few hours and results can be obtained overnight on a PC workstation.Résumé. Dans certains domaines du calcul scientifique il est nécessaire de disposer de données précises et personnalisées; les applications biomédicales en sont un bon exemple. Nous présentons ici une méthode générique permettant de générer des maillages de calculà partir de l'analyse et de la segmentation de données médicales discrètes. Si les divers composants ne sont pas nouveaux: une méthode de segmentation d'images basées sur des contours déformables et des techniques d'adaptation de maillage de surfaces et de volumes basées sur la notion de métrique, le challenge est ici d'assembler ces constituants de manièreà obtenir un code de simulation efficace et facileà utiliser.Pour des applications telles que biomédicales, l'interface utilisateur est un point clé. Dans ce projet, nous avons particulièrement soigné l'automatisation de la procédure, ce qui permet d'envisager des simulations de larges tailles pratiquement sans intervention de l'utilisateur. En particulier, les compétences de celui-ci (i.e., ses connaissances en anatomie) sont utilisées pour la segmentation des images, lesétapes de maillageétant entièrement automatisées et ne faisant intervenir qu'un ensemble réduit de paramètres (contrôle de l'approximation des surfaces notamment). * We are grateful to the CEA-LIST for funding this work

Hydrodynamic flow transition dynamics in a spacer filled filtration channel using direct numerical simulation

“…Limitations in terms of grid generation for 3D simulations arise in terms of spatial resolution, as the spacer geometry (like obtained from CT scans) is quite complex [45]. This becomes more prohibitive especially when performing flow transition studies.…”

Techniques for the Generation of 3D Finite Element Meshes of Human Organs

“…Figure 9 shows how it is possible to build a surface mesh from this set of piecewise 2D images. Pages, Sermesant and Frey (2005) describe one alternative to do the whole process: from image to surface and volume meshes.…”

“…In the following tables a summary of them is proposed, involving all the relevant aspects of meshing. Table 1 Meshing Technique Delaunay Octree-alike Marching Cubes Ruppert (1995) Ok --Schewchuk 2002aOk --Miller 2003Ok -- Abell (1999) -Ok -Ashburner (2000) -Ok - Finkel (1974) -Ok -Nesme 2008-Ok -Lorensen 1987-Ok - Chernyaev (1995) -Ok Ok Payne (1990) Ok Ok - Ferrant (1999) Ok Ok - Velasco (2007) Ok Ok Ok Couteau (2000) ---Castellano-Smith (2001) -Ok Ok Berar (2004) --- Molino (2003) Ok Ok - Audette (2007) Ok Ok Ok Pages (2005) Ok Ok Ok Alliez (2005) Ok --Chen 2004Ok --Zhang (2006 -Ok Ok Table 1 presents the reviewed techniques regarding the basic and most common meshing techniques and concepts. Note how the Delaunay property is used by all the techniques that produce tetrahedral meshes (see table 2).…”

“…Meshing type Reviewed works Registration Couteau (2000); Montagnat (2005); Castellano-Smith (2001); Berar (2004) Generation Alliez (2005); Audette (2007); Molino (2003); Abell (1999); Ashburner (2000); Velasco (2007); Pages (2005); Nesme (2008); Lorensen (1987); Payne (1990); Ferrant (1999); Montagnat (2005); Zhang (2006) Also note that several tetrahedral generators improve the quality of the input surface mesh and then continue with some point insertion strategies (using the modified input surface mesh) in order to produce the final volume mesh. In other words, techniques that use the input surface mesh, as the Advancing Front, seem to be "not so used" in the medical field.…”

“…A previous work by Pages, Sermesant and Frey in 2005, developed a general purpose technique similar to the one above. The goal was to develop an application to go from MRI and CT images to volume meshes.…”