X-ray fluorescence CT reconstruction based on residual encoder-decoder networks

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.