Patient‐specific anisotropic model of human trunk based on MR data

Abstract

SUMMARYThere are many ways to generate geometrical models for numerical simulation and most of them start with a segmentation step to extract the boundaries of the regions of interest. This paper presents an algorithm to generate a patient specific 3D geometric model, based on a tetrahedral mesh, without an initial extraction of contours from the volumetric data. Using the information directly available in the data, such as grey levels, a metric is built to drive a mesh adaptation process. The metric is used to specify the size and orientation of the tetrahedral elements everywhere in the mesh. Our method, which produces anisotropic meshes, gives good results with synthetic and real MRI data. The resulting model quality has been evaluated qualitatively and quantitatively by comparing it with an analytical solution and with a segmentation made by an expert. Results show that our method gives, in 90% of the cases, as good or better meshes as a similar isotropic method, based on the accuracy of the volume reconstruction for a given mesh size. Moreover, a comparison of Hausdorff distances between adapted meshes of both methods and ground-truth volumes shows that our method decreases reconstruction errors faster.

Semi‐automatic algorithm to build finite element numerical models of the human hearing system from Micro‐CT data

“…In recent years, numerical simulations of specific models of different pathologies in patients have been developed in various medical specialties in order to understand them more thoroughly and find more effective treatments 1–5 . This requires, in most cases, the use of images to generate the models.…”