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Author: Ru Xu

Affiliation: Yangtze Normal University, China

Keyword(s): Image Processing, Image reconstruction, Few-view CT, Total variation, Tight frame iteration.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: The excessive dose of the widely used X-ray computer tomography (CT) may induce potential disease. So lower radiation dose is an important direction of CT development. Short-scanning few-view CT (SSFW-CT) imaging can reduce radiation dose and scan time simultaneously. The total variation (TV) algorithm based on compressed sensing has been extensively used in CT reconstruction. The algorithm based on sparse transform is also applied to CT reconstruction, such as wavelet transform. Because of the serious discomfort with the SSFW-CT, the paper proposes a regularization iteration reconstruction algorithm method that combining total variation method and tight-frame transform, denoted TV-TFIR. The proposed method can reconstruct the details of the image more precisely. The simulation phantom and real data are used to verify the effectiveness of the proposed algorithm. The experimental results of the proposed method are more effective in both quantitative indicators and visual effects.

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Paper citation in several formats:
Xu, R. (2019). Total Variation and Tight Frame Iteration Regularization Based Image Reconstruction for Short-scanning Few-view Computer Tomography. In Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - CTISC; ISBN 978-989-758-357-5, SciTePress, pages 103-110. DOI: 10.5220/0008096101030110

@conference{ctisc19,
author={Ru Xu.},
title={Total Variation and Tight Frame Iteration Regularization Based Image Reconstruction for Short-scanning Few-view Computer Tomography},
booktitle={Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - CTISC},
year={2019},
pages={103-110},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008096101030110},
isbn={978-989-758-357-5},
}

TY - CONF

JO - Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - CTISC
TI - Total Variation and Tight Frame Iteration Regularization Based Image Reconstruction for Short-scanning Few-view Computer Tomography
SN - 978-989-758-357-5
AU - Xu, R.
PY - 2019
SP - 103
EP - 110
DO - 10.5220/0008096101030110
PB - SciTePress

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