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A hierarchical reconstruction of limited-view x-ray CT based on non-local structural features
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Paper Abstract

X-ray computed tomography has been recently applied to capture the dynamic behaviors of complex material systems in 4D. The dynamic 3D acquisition, however, usually leads to insufficient data acquisition with low-dose X-ray radiation and limited-angle projections. A high-fidelity CT reconstruction is challenging based on the severely limited acquisition. While prior constraint, such as local smoothness, can improve the quality of reconstructions, a more general reconstruction strategy to include structural features on a range of different scales proves to yield better reconstruction results and are more adaptive to complex structured materials. In this work, we develop the hierarchical synthesis network to establish structural priors for sparse-view CT reconstruction, which achieves high-fidelity with an improved computation efficiency. We found that the established knowledge of structural priors on each different scale can be independently transferred to sparse-view CT reconstruction under different conditions, enabling the transfer of non-local features into the reconstruction of a phase tomography application.

Paper Details

Date Published: 14 May 2019
PDF: 5 pages
Proc. SPIE 10999, Anomaly Detection and Imaging with X-Rays (ADIX) IV, 109990N (14 May 2019); doi: 10.1117/12.2519055
Show Author Affiliations
Ziling Wu, Virginia Polytechnic Institute and State Univ. (United States)
Ting Yang, Virginia Polytechnic Institute and State Univ. (United States)
Ling Li, Virginia Polytechnic Institute and State Univ. (United States)
Yunhui Zhu, Virginia Polytechnic Institute and State Univ. (United States)


Published in SPIE Proceedings Vol. 10999:
Anomaly Detection and Imaging with X-Rays (ADIX) IV
Amit Ashok; Joel A. Greenberg; Michael E. Gehm, Editor(s)

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