Compressive Spectral X-Ray CT Reconstruction via Deep Learning

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IEEE Transactions on Computational Imaging
Compressive spectral X-ray imaging (CSXI) uses coded illumination projections to reconstruct tomographic images at multiple energy bins. Different K-edge materials are used to modulate the spectrum of the X-ray source at various angles, thereby capturing the projections containing spectral attenuation information. It is a cost-effective and low-dose sensing approach; however, the image reconstruction is a nonlinear and ill-posed problem. Current methods of solving the inverse problem are computationally expensive and require extensive iterations. This paper proposes a deep learning model consisting of a set of convolutional neural networks to reconstruct the CSXI spectral images, which correspond to inpainting the subsampled sinograms, recovering the monoenergetic sinograms, and removing the artifacts from a fast but low-quality analytical reconstruction. Numerical experiments show that the proposed method significantly improves the quality of reconstructed image compared with that attained by the state-of-the-art reconstruction methods. Moreover, it significantly reduces the time-required for CSXI reconstruction.
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spectral X-ray CT, coded aperture compressive imaging, convolutional neural network, deep learning
T. Zhang, S. Zhao, X. Ma, C. M. Restrepo and G. R. Arce, "Compressive Spectral X-Ray CT Reconstruction via Deep Learning," in IEEE Transactions on Computational Imaging, vol. 8, pp. 1038-1050, 2022, doi: 10.1109/TCI.2022.3216207.