A GPU-Based Algorithm for the Simulation of X-Ray Cone-Beam Imaging

To accelerate the simulation of X-ray cone-beam imaging, a GPU(graphics processing unit) based algorithm is proposed in this paper. The algorithm generates X-ray image by accumulating the contribution of voxels along each X-ray. Intersection lengths of these voxels with X-ray are calculated by classifying the intersection types, which reduces the time-consuming dynamic branches compared to the famous incremental Siddon algorithm. To improve image quality, sampled values along X-ray are computed by GPU hardware supported linear interpolation instead of nearest interpolation used by the incremental Siddon algorithm. The experiment of the projection calculation of Shepp-logan phantom shows that the simulation speed is improved by 44% averagely as compared to the GPU-based incremental Siddon algorithm and a better image quality is achieved. Finally, the proposed algorithm is validated by the experiment with real measured data.