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    Multiclass Dictionary-Based Statistical Iterative Reconstruction for Low-Dose CT

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    Name:
    ieice_kamoshita_preprint.pdf
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    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    KAMOSHITA, Hiryu
    Kitahara, Daichi
    FUJIMOTO, Ken'ichi
    Condat, Laurent
    Hirabayashi, Akira
    KAUST Department
    Visual Computing Center (VCC)
    Visual Computing Center, King Abdullah University of Science and Technology
    Date
    2020-10-05
    Online Publication Date
    2020-10-05
    Print Publication Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/665525
    
    Metadata
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    Abstract
    This paper proposes a high-quality computed tomography (CT) image reconstruction method from low-dose X-ray projection data. A state-of-the-art method, proposed by Xu et al., exploits dictionary learning for image patches. This method generates an overcomplete dictionary from patches of standard-dose CT images and reconstructs low-dose CT images by minimizing the sum of a data fidelity and a regularization term based on sparse representations with the dictionary. However, this method does not take characteristics of each patch, such as textures or edges, into account. In this paper, we propose to classify all patches into several classes and utilize an individual dictionary with an individual regularization parameter for each class. Furthermore, for fast computation, we introduce the orthogonality to column vectors of each dictionary. Since similar patches are collected in the same cluster, accuracy degradation by the orthogonality hardly occurs. Our simulations show that the proposed method outperforms the state-of-the-art in terms of both accuracy and speed.
    Citation
    KAMOSHITA, H., KITAHARA, D., FUJIMOTO, K., CONDAT, L., & HIRABAYASHI, A. (2020). Multiclass Dictionary-Based Statistical Iterative Reconstruction for Low-Dose CT. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. doi:10.1587/transfun.2020eap1020
    Publisher
    Institute of Electronics, Information and Communications Engineers (IEICE)
    Journal
    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
    DOI
    10.1587/transfun.2020eap1020
    Additional Links
    https://www.jstage.jst.go.jp/article/transfun/advpub/0/advpub_2020EAP1020/_article
    ae974a485f413a2113503eed53cd6c53
    10.1587/transfun.2020eap1020
    Scopus Count
    Collections
    Articles; Visual Computing Center (VCC)

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