Filtering-Based Preconditioner for Accelerated High-Dimensional Cone-Beam CT Image Reconstruction

Abstract Model-based image reconstruction algorithms are known to produce high-accuracy images but are still rarely used in cone-beam computed tomography. One of the reasons for this is the computational requirements of model-based iterative algorithms, as it can take hundreds of iterations to obtain converged images. In this work, we present a measurement space-based preconditioner applied to the primal-dual hybrid gradient (PDHG) algorithm. The method is compared with the regular PDHG, FISTA, and OS-SART algorithms, as well as to a PDHG algorithm where the step-size parameters are adaptively computed. All tested algorithms utilize subsets for acceleration. The presented filtering-based preconditioner can obtain convergence in 10 iterations with 20 subsets, compared to a hundred or more iterations required by the other tested methods. The presented method is also computationally fast and has only a 15% increase in computation time per iteration compared to PDHG without the preconditioner.