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

Author:

Jiang Shanghai1,He Peng12ORCID,Deng Luzhen13ORCID,Chen Mianyi1ORCID,Wei Biao12ORCID

Affiliation:

1. Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China

2. Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China

3. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

Abstract

X-ray fluorescence computed tomography (XFCT) 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 1 mm to 9 mm. Second, discretized presentation of imaging model is established to reconstruct more accurate XFCT images. Third, XFCT images of phantoms A and B are reconstructed by filter back-projection (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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Radiology Nuclear Medicine and imaging

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