Generation of Smooth and Accurate Surface Models for Surgical Planning and Simulation

摘要

Surface models from medical image data (intensity, binary) are used for evaluating spatial relationships for intervention or radiation treatment planning. Furthermore, surface models are employed for generating volume meshes for simulating e.g. tissue deformation or blood flow. In such applications, smoothness and accuracy of the models are essential. These aspects may be influenced by image preprocessing, the mesh generation algorithm and mesh postprocessing (smoothing, simplification). Thus, we evaluated the influences of different image preprocessing methods (Gaussian smoothing, morphological operators, shape-based interpolation), model generation (Marching Cubes, Constrained Elastic Surface Nets, MPU Implicits) and mesh postprocessing to intensity and binary data with respect to its application within surgical planning and simulation. The resulting surface meshes are evaluated regarding their smoothness, accuracy and mesh quality. We consider the local curvature, equi-angle skewness, (Hausdorff) distances between two meshes (before and after processing), and volume preservation as measures. We discuss these results concerning their suitability for different applications in the field of surgical planning as well as finite element simulations and make recommendations on how to receive smooth and accurate surface meshes for exemplary cases.