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

Le Chen; Mengying Sun; Jingting Qiu; Renan Xu; Shanghai Jiang; Wenjie Nie; Wuhao Pan

doi:10.1117/12.3039729

13 September 2024 X-ray fluorescence CT reconstruction based on residual encoder-decoder networks

Le Chen, Mengying Sun, Jingting Qiu, Renan Xu, Shanghai Jiang, Wenjie Nie, Wuhao Pan

Author Affiliations +

Proceedings Volume 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024); 132541L (2024) https://doi.org/10.1117/12.3039729
Event: Fourth International Conference on Optics and Image Processing (ICOIP 2024), 2024, Chongqing, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Le Chen, Mengying Sun, Jingting Qiu, Renan Xu, Shanghai Jiang, Wenjie Nie, and Wuhao Pan "X-ray fluorescence CT reconstruction based on residual encoder-decoder networks", Proc. SPIE 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024), 132541L (13 September 2024); https://doi.org/10.1117/12.3039729

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KEYWORDS

Image restoration

Reconstruction algorithms

Detection and tracking algorithms

X-ray fluorescence spectroscopy

Education and training

Image quality

CT reconstruction

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