Combination of MCNP and SimSET for Monte Carlo simulation of SPECT with medium and high energy photons

Monte Carlo (MC) simulation has become an important tool for characterizing and evaluating imaging systems and reconstruction algorithms. In this work we combined two MC codes - MCNP and SimSET - using the advantages of each to produce a simulation tool that is efficient enough to generate SPECT dat...

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Hauptverfasser: Du, Y., Frey, E.C., Wang, W.T., Tocharoenchai, C., Baird, W.H., Tsui, B.M.W.
Format: Tagungsbericht
Sprache:eng
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Zusammenfassung:Monte Carlo (MC) simulation has become an important tool for characterizing and evaluating imaging systems and reconstruction algorithms. In this work we combined two MC codes - MCNP and SimSET - using the advantages of each to produce a simulation tool that is efficient enough to generate SPECT data. The new SimSET-MCNP method allows use of voxelized 3-D phantoms, and models photon propagation inside collimator-detector system. This combination provides a tool for evaluating compensation methods applied to imaging of agents where medium and high energy photons are important. To validate the new tool, we compared simulated projections of a sphere containing I-123 water solution embedded in a cylindrical water-filled phantom with experimentally measured projections and simulations using SimSET and MCNP alone. Using these data we compared profiles through the projection data, energy spectra, and relative number of photons in 4 projection views. The agreement with experiment was good, with disagreements on the order of a few percent. In addition, for the MC simulations we classified detected photons based on whether they scattered in the phantom, whether they passed through the collimator holes, penetrated the septa, or scattered in the collimator, and whether they resulted from 159 keV or high energy photons. For all these classes of photons there was excellent agreement between SimSET-MCNP and MCNP. Finally, we evaluated the new combination in terms of simulation time and found it significantly more efficient than MCNP alone.
ISSN:1082-3654
2577-0829
DOI:10.1109/NSSMIC.2001.1008589