Optimization of Cone Beam CT Reconstruction Algorithm Based on CUDA

Abstract: In CBCT, image reconstruction is difficult to meet the requirements of the user real-time reconstruction because data amount of image reconstruction is large, operation complexity is high and the time of reconstruction is long. CUDA based on GPU launched by NVIDIA is very suitable for large-scale parallel computing problem. This paper optimizes cone beam CT reconstruction algorithm by CUDA and improves the speed of weighted back-projection and filtering, and shortens the data access time by using the texture memory and constant memory in CUDA to respectively store the kernel function and the filtered data. The experimental results show that the reconstruction speed and the reconstruction quality are obviously improved compared with the reconstruction method based on GPU. Copyright © 2013 IFSA