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.
With the advancement of technology and medicine, X-ray CT has been widely used in medical diagnosis, treatment, and monitoring of diseases. This article aims to further optimize the performance of X-ray fluorescence CT system by studying the relationship between contrast-to-noise ratio and the concentration and size of regions of interest (ROI). Using Geant4 XFCT simulation modeling, this study analyzes the impact of ROI concentration and size on the quality of XFCT reconstructed images. To assess the influence of different ROI concentrations on imaging performance of the X-ray fluorescence CT system, the simulation modeling system was adjusted for different ROI sizes, and twenty experimental groups were conducted. The results indicate that ROI concentration and size have a significant impact on imaging quality. Under specific conditions of concentration and size within the region of interest, optimal imaging effects of X-ray fluorescence CT can be achieved. These two factors interact, and when adjusting the parameters of ROI concentration and size to optimize imaging quality, it is necessary to consider the changes in both parameters rather than just the influence of a single parameter.
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