Compressive Spectral X-Ray CT Reconstruction via Deep Learning
| Author(s) | Zhang, Tong | |
| Author(s) | Zhao, Shengjie | |
| Author(s) | Ma, Xu | |
| Author(s) | Restrepo, Carlos | |
| Author(s) | Arce, Gonzalo R. | |
| Date Accessioned | 2022-12-20T18:33:24Z | |
| Date Available | 2022-12-20T18:33:24Z | |
| Publication Date | 2022-10-20 | |
| Description | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This article was originally published in IEEE Transactions on Computational Imaging. The version of record is available at: https://doi.org/10.1109/TCI.2022.3216207 | |
| Abstract | 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. | |
| Sponsor | T. Zhang, and S. Zhao were supported in part by the National Key Research and Development Project under Grant 2019YFB2102300, in part by the National Natural Science Foundation of China under Grant 61936014, in part by Fundamental Research Funds for the Central Universities, in part by Shanghai Municipal Science and Technology Major Project No. 2021SHZDZX0100. (Corresponding author: Shengjie Zhao). G. R. Arce and C. Restrepo were supported by the National Science Foundation (NSF) (CIF 1717578). | |
| Citation | 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. | |
| ISSN | 2333-9403 | |
| URL | https://udspace.udel.edu/handle/19716/31833 | |
| Language | en_US | |
| Publisher | IEEE Transactions on Computational Imaging | |
| Keywords | spectral X-ray CT | |
| Keywords | coded aperture compressive imaging | |
| Keywords | convolutional neural network | |
| Keywords | deep learning | |
| Title | Compressive Spectral X-Ray CT Reconstruction via Deep Learning | |
| Type | Article |
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