Po‐Poster ‐ 20: Octree based compression method of DICOM images for voxel number reduction and faster Monte Carlo simulations

Abstract

Purpose: Diminish the number of voxels created from a set of DICOM images while keeping critical information at tissue interfaces needed in a Monte Carlo simulation without compromising the physical quality of the voxelized image. An algorithm was developed to apply an octree compression to DICOM images. The algorithm works as follows: the whole set of DICOM image is assumed as a cube. It is then split in eight equal smaller cubes. Each of the cubes is checked for density homogeneity. If a high density gradient is encountered in a cube, it is also split in eight equal parts. This process goes on until the minimum specified voxel size is reach. The resulting image is composed of various voxels size. To verify precision, Monte Carlo simulation with GEANT4 using a narrow beam passing through several high density gradients was done. The resulting number of voxels range from 5 to 20% of the original size depending on configuration. The voxel area on a typical slice is 1 square voxel (i.e. no compression) at high density gradient to 64 square voxel for homogeneous sections. Mean volume was about 5.43 voxels for the phantom used. Monte Carlo simulation shows less then 1% difference in dose at the high gradient interface. The octree compression is an excellent method to compress DICOM information for Monte Carlo treatment planning without losing precision at the interfaces in homogeneous regions. Given good parameters the algorithm also has the ability to smooth digital noise in the homogenous area.