9 March 2017 Method for decreasing CT simulation time of complex phantoms and systems through separation of material specific projection data
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Abstract
Computer simulation is a powerful tool in CT; however, long simulation times of complex phantoms and systems, especially when modeling many physical aspects (e.g., spectrum, finite detector and source size), hinder the ability to realistically and efficiently evaluate and optimize CT techniques. Long simulation times primarily result from the tracing of hundreds of line integrals through each of the hundreds of geometrical shapes defined within the phantom. However, when the goal is to perform dynamic simulations or test many scan protocols using a particular phantom, traditional simulation methods inefficiently and repeatedly calculate line integrals through the same set of structures although only a few parameters change in each new case. In this work, we have developed a new simulation framework that overcomes such inefficiencies by dividing the phantom into material specific regions with the same time attenuation profiles, acquiring and storing monoenergetic projections of the regions, and subsequently scaling and combining the projections to create equivalent polyenergetic sinograms. The simulation framework is especially efficient for the validation and optimization of CT perfusion which requires analysis of many stroke cases and testing hundreds of scan protocols on a realistic and complex numerical brain phantom. Using this updated framework to conduct a 31-time point simulation with 80 mm of z-coverage of a brain phantom on two 16-core Linux serves, we have reduced the simulation time from 62 hours to under 2.6 hours, a 95% reduction.
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Sarah E. Divel, Sarah E. Divel, Soren Christensen, Soren Christensen, Max Wintermark, Max Wintermark, Maarten G. Lansberg, Maarten G. Lansberg, Norbert J. Pelc, Norbert J. Pelc, } "Method for decreasing CT simulation time of complex phantoms and systems through separation of material specific projection data", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 1013259 (9 March 2017); doi: 10.1117/12.2254076; https://doi.org/10.1117/12.2254076
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