This paper presents a numerical coarsening method for corotational elasticity, which enables interactive large deformation of high-resolution heterogeneous objects. Our method derives a coarse elastic model from a high-resolution discretization of corotational elasticity with high-resolution boundary conditions. This is in contrast to previous coarsening methods, which derive a coarse elastic model from an unconstrained high-resolution discretization of regular linear elasticity, and then apply corotational computations directly on the coarse setting. We show that previous approaches fail to handle high-resolution boundary conditions correctly, suffering accuracy and robustness problems. Our method, on the other hand, supports efficiently accurate high-resolution boundary conditions, which are fundamental for rich interaction with high-resolution heterogeneous models. We demonstrate the potential of our method for interactive deformation of complex medical imaging data sets.

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Figure 1: On the left, a 3D medical image with the nodes of a tetrahedral mesh overlaid. The next four snapshots show, from left to right, interactive deformations of a kidney, the heart, and abdominal vessels. The 256 × 160 × 122 volume is deformed at 67fps. AbstractMany inherently deformable structures, such as human anatomy, are often represented using a regular volumetric discretization, e.g., in medical imaging. While deformation algorithms employ discretizations that deform themselves along with the material, visualization algorithms are optimized for regular undeformed discretizations. In this paper, we propose a method to transform highresolution volume data embedded in a deformable tetrahedral mesh. We cast volume deformation as a problem of tetrahedral rasterization with 3D texture mapping. Then, the core of our solution to volume data deformation is a very fast algorithm for tetrahedral rasterization. We perform rasterization as a massively parallel operation on target voxels, and we minimize the number of voxels to be handled using a multi-resolution culling approach. Our method allows the deformation of volume data with over 20 million voxels at interactive rates.

This chapter will concentrate on the advantages that VR can offer to the Healthcare Sector. After a brief introduction, the second section will present an analysis of the areas where VR techniques can be successfully applied. Next section will describe some existing VR applications in healthcare. The development of a VR surgery simulator, with all the aspects that make this process challenging, will be presented on the following section. An analysis of the difficulties specific to healthcare environments while dealing with the design and development of VR applications will be covered in the next section. The last section will be devoted to conclusions and future perspectives of VR in the Healthcare Sector.

The use of three-dimensional digitizers in computer vision and CAD systems produces an object description consisting of a collection of scattered points in Ê 3. In order to obtain a representation of the objects' surface it is necessary to establish a procedure that allows the recovering of their continuity, lost during the data acquisition process. A full automatic O(n 2) algorithm is presented. Such algorithm obtains surface representations of free genus objects described from a set of points that belong to the original surface of the object. The only information available about each point is its position in Ê 3. The achieved surface is a Delaunay triangulation of the initial cloud of points. The algorithm has been successfully applied to three-dimensional data proceeding from synthetic and real free shape objects.