An analytical simulation method for X-ray fluorescence computed tomography

X-ray Fluorescence Computed Tomography (XFCT) is an emerging imaging technique that provides elemental distribution in objects non-invasively. To predict and optimize the experimental outcome of XFCT, data generated by Monte Carlo (MC) simulation is usually used. However, MC codes typically require extended computing times to generate sufficient data. In this paper, we report an XFCT simulation method based on analytical computation. By implementing it in GPU, the time required for the simulation of fluorescence sinograms has been sharply reduced. Through comparing the data generated by the technique presented here with data obtained through other approaches, its validity have been verified. It is expected that the simulation method proposed here could serve as a supporting tool for XFCT in aspects such as optimizing the scanning parameters for XFCT, confirming the feasibility of sample studied by XFCT and developing novel XFCT reconstruction algorithms.X-ray Fluorescence Computed Tomography (XFCT) is an emerging imaging technique that provides elemental distribution in objects non-invasively. To predict and optimize the experimental outcome of XFCT, data generated by Monte Carlo (MC) simulation is usually used. However, MC codes typically require extended computing times to generate sufficient data. In this paper, we report an XFCT simulation method based on analytical computation. By implementing it in GPU, the time required for the simulation of fluorescence sinograms has been sharply reduced. Through comparing the data generated by the technique presented here with data obtained through other approaches, its validity have been verified. It is expected that the simulation method proposed here could serve as a supporting tool for XFCT in aspects such as optimizing the scanning parameters for XFCT, confirming the feasibility of sample studied by XFCT and developing novel XFCT reconstruction algorithms.A