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Sparse-View CT Reconstruction Based on Improved Re-Sidual Network
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID. Nanjing University of Posts and Telecommunications, Nanjing, China.ORCID iD: 0000-0003-3779-5647
2020 (English)In: Computational and Experimental Simulations in Engineering: Proceedings of ICCES2019, Springer, 2020, p. 1069-1080Chapter in book (Refereed)
Abstract [en]

With the development of CT imaging, people have higher requirements for the quality of CT image reconstruction. It is desirable to use as low as reasonably achievable X-ray dose while meeting the quality of imaging requirements. Sparse-view reconstruction is a valid measure to resolve the radiation dose problem. Owing to the angular range of projection data does not satisfy the data completeness condition, sparse-view reconstruction has always been a conundrum in CT image reconstruction. In this paper, we introduces a new CT sparse-view reconstruction algorithm, which bases on the residual network. We optimize traditional residual models by improving the superfluous modules and reducing unnecessary calculations. Compared to several other classic methods, the experimental results with our network obtained better consequent, regarding artifact reduction, feature preservation, and computational speed.

Place, publisher, year, edition, pages
Springer, 2020. p. 1069-1080
Series
Mechanisms and Machine Science, ISSN 2211-0984 ; 75
Keywords [en]
CT image reconstruction, Residual network, Sparse-view
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-267863DOI: 10.1007/978-3-030-27053-7_92Scopus ID: 2-s2.0-85075553053ISBN: 978-3-030-27052-0 (print)OAI: oai:DiVA.org:kth-267863DiVA, id: diva2:1397129
Note

QC 20200227

Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-02-27Bibliographically approved

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Li, Haibo

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