Iterative reconstruction of cone-beam CT data on a cluster

Three-dimensional iterative reconstruction of large CT data sets poses several challenges in terms of the associated computational and memory requirements. In this paper, we present results obtained by implementing a computational framework for reconstructing axial cone-beam CT data using a cluster of inexpensive dual-processor PCs. In particular, we discuss our parallelization approach, which uses POSIX threads and message passing (MPI) for local and remote load distribution, as well as the interaction of that load distribution with the implementation of ordered subset based algorithms. We also consider a heuristic data-driven 3D focus of attention algorithm that reduces the amount of data that must be considered for many data sets. Furthermore, we present a modification to the SIRT algorithm that reduces the amount of data that must be communicated between processes. Finally, we introduce a method of separating the work in such a way that some computation can be overlapped with the MPI communication thus further reducing the overall run-time. We summarize the performance results using reconstructions of experimental data.