The purpose of the present study is to introduce a compression algorithm for the CT (computed tomography) data used in Monte Carlo simulations. Performing simulations on the CT data implies large computational costs as well as large memory requirements since the number of voxels in such data reaches typically into hundreds of millions voxels. CT data, however, contain homogeneous regions which could be regrouped to form larger voxels without affecting the simulation's accuracy. Based on this property we propose a compression algorithm based on octrees: in homogeneous regions the algorithm replaces groups of voxels with a smaller number of larger voxels. This reduces the number of voxels while keeping the critical high-density gradient area. Results obtained using the present algorithm on both phantom and clinical data show that compression rates up to 75% are possible without losing the dosimetric accuracy of the simulation.