Fast parallel image reconstruction for cone-beam FDK algorithm

FDK algorithm is a popular analytical reconstruction method for practical cone-beam CT scanners. Compared with iterative methods, the FDK algorithm is computationally efficient. However, the reconstruction speed remains a limitation for its application when dealing with high resolution images. In this paper, we propose a fast method for parallel implementation of the FDK algorithm by the use of multi-GPU. First, we optimize the backprojection operation of FDK according to the property of geometric symmetry and the correlation between adjacent slices. Then, we utilize the multi-thread technology to realize the parallel implementation of the optimized FDK algorithm on multi-GPU. Finally, we implement the proposed method on a multi-GPU platform. Numerical experiment shows that the proposed multi-GPU-based approach can reconstruct a 512 cubed volume in 1.9seconds from 360 projections of resolution 512 x512, which is 511 times faster than a traditional CPU-based approach and 5 times faster than a single GPU-based approach. In addition, the reconstruction results also indicate that the proposed method can maintain the same precision with traditional method.