A Robust Algorithm for Voxel-to-Polygon Mesh Phantom Conversion

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Abstract

Over the past 20 years, the coupling of computational human phantoms within existing Monte Carlo radiation transport codes has required phantoms to be in a voxelized format. Very recently, however, several popular radiation transport codes such as MCNP6, GEANT4, and PHITS now facilitate direct radiation transport in phantoms that are represented by either polygon or tetrahedral mesh structures - surfaces and volumes, respectively, that define both the phantom’s body contour and its array of internal organs. While both voxel-based and mesh-based phantoms provide a high degree of anatomic realism as compared to first-generation stylized (or mathematical) phantoms, mesh-type phantoms are now considered the state of the art as they permit re-sculpting of individual organs, body regions, and overall body size and shape. They also allow for extremity articulations and inclusion of thin tissue layers of radiobiological importance. These are all features that are either not permitted in or are not readily available to voxel-based phantoms. Nevertheless, since the late 1980s, a tremendous number of voxel-based computational human phantoms have been developed from image segmentation of patient CT or MR data. As a result, there is a need for conversion of these existing voxel phantoms to mesh-type formats. The present work describes an efficient and accurate algorithm to convert voxel-based phantoms to mesh-based formats, thus permitting the user to take full advantage of these additional modeling features. For this conversion, a boundary detection algorithm was implemented in conjunction with polygon detection to form high-quality mesh data suitable for radiation transport simulation or finite element analysis. This conversion can result in a significant reduction of required simulation time and can allow current voxel data to be used in modern CAD software.

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APA

Brown, J. L., Furuta, T., & Bolch, W. E. (2019). A Robust Algorithm for Voxel-to-Polygon Mesh Phantom Conversion. In Brain and Human Body Modeling: Computational Human Modeling at EMBC 2018 (pp. 317–327). Springer International Publishing. https://doi.org/10.1007/978-3-030-21293-3_17

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