14 March 2018 Simulation tools for scattering corrections in spectrally resolved x-ray computed tomography using McXtrace
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Abstract
Spectral computed tomography is an emerging imaging method that involves using recently developed energy discriminating photon-counting detectors (PCDs). This technique enables measurements at isolated high-energy ranges, in which the dominating undergoing interaction between the x-ray and the sample is the incoherent scattering. The scattered radiation causes a loss of contrast in the results, and its correction has proven to be a complex problem, due to its dependence on energy, material composition, and geometry. Monte Carlo simulations can utilize a physical model to estimate the scattering contribution to the signal, at the cost of high computational time. We present a fast Monte Carlo simulation tool, based on McXtrace, to predict the energy resolved radiation being scattered and absorbed by objects of complex shapes. We validate the tool through measurements using a CdTe single PCD (Multix ME-100) and use it for scattering correction in a simulation of a spectral CT. We found the correction to account for up to 7% relative amplification in the reconstructed linear attenuation. It is a useful tool for x-ray CT to obtain a more accurate material discrimination, especially in the high-energy range, where the incoherent scattering interactions become prevailing (>50  keV).
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Matteo Busi, Matteo Busi, Ulrik L. Olsen, Ulrik L. Olsen, Erik B. Knudsen, Erik B. Knudsen, Jeppe R. Frisvad, Jeppe R. Frisvad, Jan Kehres, Jan Kehres, Erik S. Dreier, Erik S. Dreier, Mohamad Khalil, Mohamad Khalil, Kristoffer Haldrup, Kristoffer Haldrup, } "Simulation tools for scattering corrections in spectrally resolved x-ray computed tomography using McXtrace," Optical Engineering 57(3), 037105 (14 March 2018). https://doi.org/10.1117/1.OE.57.3.037105 . Submission: Received: 25 October 2017; Accepted: 14 February 2018
Received: 25 October 2017; Accepted: 14 February 2018; Published: 14 March 2018
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