Marching cube algorithm: review and trilinear interpolation adaptation for image-based dosimetric models.

Abstract

Current internal organ dose assessment methodologies utilize three-dimensional (3D) medical images of the body to model organ shapes and tissue interfaces. These models are coupled to computer programs that measure radionuclide energy deposition or chord-length distributions directly within these images. Previous studies have shown that the rectangular shape of image voxels generates voxel effects that alter the outcome of these calculations. To minimize voxel effects, the present study proposes to use the Marching Cube (MC) algorithm to generate isosurfaces delineating tissue interfaces from the gray-level images. First, a review of the different techniques surrounding the MC algorithm is presented. Next, an adaptation of the algorithm is proposed in which a trilinear interpolation of the gray levels is used to generate a hyperboloid surface within the MCs. This new technique is shown to solve the classic ambiguity problem of the MC algorithm and also to reduce the data size inherent to the triangulated surface. It also provides a simple algorithm to accurately measure distances within the image. The technique is then tested with a mathematical model of trabecular bone. The trilinear interpolation method is shown to remove voxel effects and to produce reliable chord-length distributions across image regions. The technique is thus recommended for use with digital medical images needed for internal radiation transport simulations. The current study is performed for a single isosurface that separates two media within the same image, but it is proposed that the technique can be extended to multiple isosurfaces that delineate several organs or organ regions within 3D tomographic voxels of human anatomy.