X-ray Fluorescence Computed Tomography (XFCT) is a molecular imaging technique which is used to reconstruct the distribution of trace elements in samples based on fluorescence signals. However, the quality of reconstructed images is compromised due to sample absorption. In this paper, we propose a deep learning-based XFCT image reconstruction framework to directly transform from the sinogram domain to the image domain, enabling fast reconstruction of XFCT and addressing the fluorescence attenuation issue. Through numerical simulation experiments, it is demonstrated that the Red CNN algorithm improves the NMSE and PSNR evaluation metrics by 0.0249 and 1.3768, respectively, compared to FBP and MLEM methods.
X-ray fluorescence computed tomography (XFCT), as a non-invasive imaging technique, has attracted much attention for its simulation and reconstruction. In this study, we built a simulation model of cone-beam X-ray fluorescence CT using Geant4 and simulated the propagation and interaction process of X-rays in the phantom by Monte Carlo simulation. Then, we acquired the projection data and used the FDK (Feldkamp-Davis-Kress) resolution algorithm to reconstruct the images in three dimensions. The results show that cone-beam X-ray fluorescence CT combined with the FDK algorithm can effectively reconstruct the images, which provides strong support for non-invasive imaging and trace element distribution analysis.
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