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dc.contributor.authorKAMOSHITA, Hiryu
dc.contributor.authorKitahara, Daichi
dc.contributor.authorFUJIMOTO, Ken'ichi
dc.contributor.authorCondat, Laurent
dc.contributor.authorHirabayashi, Akira
dc.date.accessioned2020-10-11T11:16:41Z
dc.date.available2020-10-11T11:16:41Z
dc.date.issued2020-10-05
dc.identifier.citationKAMOSHITA, 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
dc.identifier.issn0916-8508
dc.identifier.issn1745-1337
dc.identifier.doi10.1587/transfun.2020eap1020
dc.identifier.urihttp://hdl.handle.net/10754/665525
dc.description.abstractThis 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.
dc.publisherInstitute of Electronics, Information and Communications Engineers (IEICE)
dc.relation.urlhttps://www.jstage.jst.go.jp/article/transfun/advpub/0/advpub_2020EAP1020/_article
dc.titleMulticlass Dictionary-Based Statistical Iterative Reconstruction for Low-Dose CT
dc.typeArticle
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentVisual Computing Center, King Abdullah University of Science and Technology
dc.identifier.journalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
dc.eprint.versionPost-print
dc.contributor.institutionDept. of Information Science and Engineering, Ritsumeikan University
dc.contributor.institutionDept. of Engineering and Design, Kagawa University
kaust.personCONDAT, Laurent
refterms.dateFOA2020-10-13T06:50:31Z
dc.date.published-online2020-10-05
dc.date.published-print2020


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