A Monte Carlo Model of a Benchtop X-Ray Fluorescence Computed Tomography System and Its Application to Validate a Deconvolution-Based X-Ray Fluorescence Signal Extraction Method.

In this study, we developed and validated a Geant4-based Monte Carlo (MC) model of an experimental benchtop X-ray fluorescence (XRF) computed tomography (XFCT) system for quantitative imaging of metallic nanoparticles such as gold nanoparticles (GNPs) injected into small animals for preclinical testing of various NP-based diagnostic and therapeutic approaches. Detailed hardware components of the current benchtop XFCT system, including the X-ray source, excitation beam collimation and filtration, custom imaging phantoms with GNP solutions, and single/ring/linear array detectors with custom collimation, were incorporated into the MC model. In conjunction with a known CdTe detector response function, a deconvolution-based XRF signal extraction method was also developed in this study, which enabled complete separation of gold K-shell XRF peaks even when they almost overlapped and facilitated extraction of XRF signals from a broadband Compton scattered photon background. The extracted signal-to-background ratios were comparable with those expected using an ideal detector with high enough energy resolution (e.g., 0.1 keV full-width at half-maximum). Once convoluted with the CdTe detector response function, the MC-calculated spectra for excitation beams or emitted photons and XFCT image spatial resolutions agreed well with those measured experimentally. Thus, the current MC model can be used to optimize the beam/imaging parameters (e.g., beam geometry, excitation X-ray beam energy, and X-ray filter material) as well as the design of critical hardware components (e.g., detector collimators) within the current benchtop XFCT system. Also, the current XRF signal extraction method can relax the usual stringent requirement of detector energy resolution while not degrading the sensitivity of benchtop XFCT.