Abstract
After we briefly review representative analytic and iterative reconstruction algorithms for X-ray computed tomography (CT), we address the need for faster reconstruction by parallel computing techniques. For a decent, a conebeam reconstruction usually takes hours on a regular PC, since most of algorithms take more than 60 iterations even longer. In order to speedup the performance, people introduce various acceleration methodologies including algorithm improvements, chip utilization, and parallel computing technique. This paper focuses on the speedup the computation using parallel computing. The first generation of parallel computing systems was based on a centralized parallel configuration. The second generation of such systems employed a cluster of general-purpose computers that are connected by a fast local area network (LAN). Hereby, we highlight distributed parallel computing techniques: from a locally distributed client-server topology to a peer-to-peer (P2P) enhanced network model. With the P2P technology, the client would be directly connected to all other computing peers seamlessly, forming a virtual parallel computer. There are multiple Internet connections between the client and other computing peers. This way, a single failure of node wouldn ’ t cause the entire failure of computation. Finally, we state that by integrating the large-scale geographically distributed systems such as Grid computing technology the future of the CT reconstruction will be highly parallel, efficient, scalable over the Internet, so will be other biomedical imaging tasks.
Keywords: X-ray computed tomography (CT), multi-slice spiral CT (MSCT), filtered back-projection (FBP), iterative reconstruction (IR), peer-to-peer (P2P) technology
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