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

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

Photonic Material Selection of Scintillation Crystals Using Monte Carlo Method for X-Ray Detection in Industrial Computed Tomography

Currently industrial X-CT system is designed according to characteristics of test objects, and test objects determine industrial X-CT system structure, X-ray detector/sensor property, scanning mode, and so forth. So there are no uniform standards for the geometry size of scintillation crystals of detector. Moreover, scintillation crystals are usually mixed with some highly toxic impurity elements, such as Tl and Cd. Thus, it is indispensable for establishing guidelines of engineering practice to simulate X-ray detection performances of different scintillation crystals. This paper focuses on how to achieve high efficient X-ray detection in industrial X-CT system which used Monte Carlo (MC) method to study X-ray energy straggling characteristics, full energy peak efficiency, and conversion efficiency of some scintillation crystals (e.g., CsI(Tl), NaI(Tl), and CdWO4) after X-ray interacted with these scintillation crystals. Our experimental results demonstrate that CsI(Tl) scintillation crystal has the advantages of conversion efficiency, spectral matching, manufacturing process, and full energy peak efficiency; it is an ideal choice for high efficient X-ray detection in industrial X-CT system

Material Discrimination Based on K-edge Characteristics

Spectral/multienergy CT employing the state-of-the-art energy-discriminative photon-counting detector can identify absorption features in the multiple ranges of photon energies and has the potential to distinguish different materials based on K-edge characteristics. K-edge characteristics involve the sudden attenuation increase in the attenuation profile of a relatively high atomic number material. Hence, spectral CT can utilize material K-edge characteristics (sudden attenuation increase) to capture images in available energy bins (levels/windows) to distinguish different material components. In this paper, we propose an imaging model based on K-edge characteristics for maximum material discrimination with spectral CT. The wider the energy bin width is, the lower the noise level is, but the poorer the reconstructed image contrast is. Here, we introduce the contrast-to-noise ratio (CNR) criterion to optimize the energy bin width after the K-edge jump for the maximum CNR. In the simulation, we analyze the reconstructed image quality in different energy bins and demonstrate that our proposed optimization approach can maximize CNR between target region and background region in reconstructed image

A CT Reconstruction Algorithm Based on L 1/2

Computed tomography (CT) reconstruction with low radiation dose is a significant research point in current medical CT field. Compressed sensing has shown great potential reconstruct high-quality CT images from few-view or sparse-view data. In this paper, we use the sparser L1/2 regularization operator to replace the traditional L1 regularization and combine the Split Bregman method to reconstruct CT images, which has good unbiasedness and can accelerate iterative convergence. In the reconstruction experiments with simulation and real projection data, we analyze the quality of reconstructed images using different reconstruction methods in different projection angles and iteration numbers. Compared with algebraic reconstruction technique (ART) and total variance (TV) based approaches, the proposed reconstruction algorithm can not only get better images with higher quality from few-view data but also need less iteration numbers