Abstract
We propose a framework using freely available tools for the synthesis of physically realistic CT measurements for low dose reconstruction development and validation, using a fully sampled Monte Carlo method. This allows the generation of test data that has artefacts such as photon starvation, beam-hardening and scatter, that are both physically realistic and not unfairly biased towards model-based iterative reconstruction (MBIR) algorithms. Using the open source Monte Carlo tool GATE and spectrum simulator SpekCalc, we describe how physical elements such as source, specimen and detector may be modelled, and demonstrate the construction of fan-beam and cone-beam CT systems. We then show how this data may be consolidated and used with image reconstruction tools. We give examples with a low dose polyenergetic source, and quantitatively analyse reconstructions against the numerical ground-truth for MBIR with simulated and ‘inverse crime’ data. The proposed framework offers a flexible and easily reproducible tool to aid MBIR development, and may reduce the gap between synthetic and clinical results.
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Acknowledgements
This work was supported by the Maxwell Advanced Technology Fund, EPSRC DTP studentship funds and ERC project: C-SENSE (ERC-ADG-2015-694888). MD is also supported by a Royal Society Wolfson Research Merit Award.
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Mason, J.H., Nailon, W.H., Davies, M.E. (2017). A Monte Carlo Framework for Low Dose CT Reconstruction Testing. In: Tsaftaris, S., Gooya, A., Frangi, A., Prince, J. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2017. Lecture Notes in Computer Science(), vol 10557. Springer, Cham. https://doi.org/10.1007/978-3-319-68127-6_9
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DOI: https://doi.org/10.1007/978-3-319-68127-6_9
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