# Monte Carlo Simulation for Polychromatic X-ray Fluorescence Computed Tomography with Sheet-Beam Geometry

## TL;DR

This study uses Monte Carlo simulation to evaluate polychromatic X-ray fluorescence computed tomography with sheet-beam geometry, demonstrating its feasibility and the effectiveness of a discretized imaging model for accurate image reconstruction.

## Contribution

First application of Monte Carlo simulation to polychromatic sheet-beam XFCT, introducing a discretized imaging model for improved accuracy.

## Key findings

- Feasibility of sheet-beam XFCT with polychromatic X-ray source.
- Discretized imaging model improves image reconstruction accuracy.
- Reconstruction methods (FBP and MLEM) effectively produce images with good contrast-to-noise ratio.

## Abstract

X-ray fluorescence computed tomography based on sheet-beam can save a huge amount of time to obtain a whole set of projections using synchrotron. However, it is clearly unpractical for most biomedical research laboratories. In this paper, polychromatic X-ray fluorescence computed tomography with sheet-beam geometry is tested by Monte Carlo simulation. First, two phantoms (A and B) filled with PMMA are used to simulate imaging process through GEANT 4. Phantom A contains several GNP-loaded regions with the same size (10 mm) in height and diameter but different Au weight concentration ranging from 0.3% to 1.8%. Phantom B contains twelve GNP-loaded regions with the same Au weight concentration (1.6%) but different diameter ranging from 1mm to 9mm. Second, discretized presentation of imaging model is established to reconstruct more accurate XFCT images. Third, XFCT images of phantom A and B are reconstructed by fliter backprojection (FBP) and maximum likelihood expectation maximization (MLEM) with and without correction, respectively. Contrast to noise ratio (CNR) is calculated to evaluate all the reconstructed images. Our results show that it is feasible for sheet-beam XFCT system based on polychromatic X-ray source and the discretized imaging model can be used to reconstruct more accurate images.

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Source: https://tomesphere.com/paper/1703.01455