New GPU implementation of Separable Footprint (SF) Projector and Backprojector : first results

International audience—Model-based iterative reconstruction methods enable to improve the quality of reconstruction in 3D X-ray Computed Tomography (CT). The main computational burden of these methods lies in successive projection and backprojection operations. Among existing pairs of projector and backprojector, Separable Footprint (SF) pair combines computational efficiency and accurate modelling of X-rays passing through the volume to image. In order to accelerate these operators, implementations on Graphical Processor Units (GPUs) for parallel-computing have been proposed for SF pair. Due to a CPU-loop, these implementations involve many memory transfers between CPU and GPU which are known to be the main bottleneck for GPU computing. In this paper, we investigate a new GPU implementation of SF projector and backprojector in order to minimize these memory transfers. Our proposed GPU SF projector and backprojector have no CPU-loop, and use two ray-driven kernels for the projection and one voxel-driven kernel for the backprojection. After having described their implementations, we study these operators as single modules and validate it in a MBIR method. Perspectives for this work are GPU optimizations and comparisons with the other existing implementations of SF pair