A GPU-optimized binary space partition structure to accelerate the Monte Carlo simulation of CT projections of voxelized patient models with metal implants

Monte Carlo x-ray transport simulation codes can generate radiographic images that are equivalent to images produced by clinical systems. Most codes optimized for medical imaging use voxels to represent the patient anatomy and employ delta scattering (Woodcock algorithm) as an essential acceleration technique. With delta scattering all voxels have the same attenuation and x rays cross multiple voxels in each step reducing the time spent accessing memory and computing voxel interfaces. A drawback of this approach is that it is inefficient in phantoms with highly attenuating voxels. We present a binary space partition structure, bitree, that improves the performance of delta scattering by selecting an optimum attenuation within different regions while minimizing the interfaces to be crossed. The described bitree and its traversal algorithm are optimized for GPU-computing and have been implemented in the PENELOPE-based MC-GPU code. The bitree approach reduced the execution time by 88.6% for the simulation of a head CT scan with a metallic implant.