Region-oriented CT image representation for reducing computing time of Monte Carlo simulations

We propose a new method for efficient particle transportation in voxelized geometry for Monte Carlo simulations. We describe its use for calculating dose distribution in CT images for radiation therapy.

The proposed approach, based on an implicit volume representation named segmented volume, coupled with an adapted segmentation procedure and a distance map, allows us to minimize the number of boundary crossings, which slows down simulation. The method was implemented with the GEANT4 toolkit and compared to four other methods: One box per voxel, parameterized volumes, octree-based volumes, and nested parameterized volumes. For each representation, we compared dose distribution, time, and memory consumption.

The proposed method allows us to decrease computational time by up to a factor of 15, while keeping memory consumption low, and without any modification of the transportation engine. Speeding up is related to the geometry complexity and the number of different materials used. We obtained an optimal number of steps with removal of all unnecessary steps between adjacent voxels sharing a similar material. However, the cost of each step is increased. When the number of steps cannot be decreased enough, due for example, to the large number of material boundaries, such a method is not considered suitable.

This feasibility study shows that optimizing the representation of an image in memory potentially increases computing efficiency. We used the GEANT4 toolkit, but we could potentially use other Monte Carlo simulation codes. The method introduces a tradeoff between speed and geometry accuracy, allowing computational time gain. However, simulations with GEANT4 remain slow and further work is needed to speed up the procedure while preserving the desired accuracy.