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Article: Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning

TitleImage-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning
Authors
KeywordsComputed tomography
Image reconstruction
Tensile stress
X-ray imaging
Machine learning
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7433213
Citation
IEEE Transactions on Radiation and Plasma Medical Sciences, 2020, Epub 2020-05-26 How to Cite?
AbstractThe spectral computed tomography (CT) has huge advantages by providing accurate material information. Unfortunately, due to the instability or overdetermination of material decomposition model, the accuracy of material decomposition can be compromised in practice. Very recently, the dictionary learning based image-domain material decomposition (DLIMD) can obtain high accuracy for material decompositions from reconstructed spectral CT images. This method can explore the correlation of material components to some extent by training a unified dictionary from all material images. In addition, the dictionary learning based prior as a penalty is applied on material components independently, and many parameters would be carefully elaborated in practice. Because the concentration of contrast agent in clinical applications is low, it can result in data inconsistency for dictionary based representation during the iteration process. To avoid the aforementioned limitations and further improve the accuracy of materials, we first construct a generalized dictionary learning based image-domain material decomposition (GDLIMD) model. Then, the material tensor image is unfolded along the mode-1 to enhance the correlation of different materials. Finally, to avoid the data inconsistency of low iodine contrast, a normalization strategy is employed. Both physical phantom and tissue-synthetic phantom experiments demonstrate the proposed GDLIMD method outperforms the DLIMD and direct inversion (DI) methods.
Persistent Identifierhttp://hdl.handle.net/10722/283363
ISSN

 

DC FieldValueLanguage
dc.contributor.authorWu, W-
dc.contributor.authorChen, P-
dc.contributor.authorWang, S-
dc.contributor.authorVardhanabhuti, V-
dc.contributor.authorLiu, F-
dc.contributor.authorYu, H-
dc.date.accessioned2020-06-22T02:55:30Z-
dc.date.available2020-06-22T02:55:30Z-
dc.date.issued2020-
dc.identifier.citationIEEE Transactions on Radiation and Plasma Medical Sciences, 2020, Epub 2020-05-26-
dc.identifier.issn2469-7311-
dc.identifier.urihttp://hdl.handle.net/10722/283363-
dc.description.abstractThe spectral computed tomography (CT) has huge advantages by providing accurate material information. Unfortunately, due to the instability or overdetermination of material decomposition model, the accuracy of material decomposition can be compromised in practice. Very recently, the dictionary learning based image-domain material decomposition (DLIMD) can obtain high accuracy for material decompositions from reconstructed spectral CT images. This method can explore the correlation of material components to some extent by training a unified dictionary from all material images. In addition, the dictionary learning based prior as a penalty is applied on material components independently, and many parameters would be carefully elaborated in practice. Because the concentration of contrast agent in clinical applications is low, it can result in data inconsistency for dictionary based representation during the iteration process. To avoid the aforementioned limitations and further improve the accuracy of materials, we first construct a generalized dictionary learning based image-domain material decomposition (GDLIMD) model. Then, the material tensor image is unfolded along the mode-1 to enhance the correlation of different materials. Finally, to avoid the data inconsistency of low iodine contrast, a normalization strategy is employed. Both physical phantom and tissue-synthetic phantom experiments demonstrate the proposed GDLIMD method outperforms the DLIMD and direct inversion (DI) methods.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7433213-
dc.relation.ispartofIEEE Transactions on Radiation and Plasma Medical Sciences-
dc.rightsIEEE Transactions on Radiation and Plasma Medical Sciences. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectComputed tomography-
dc.subjectImage reconstruction-
dc.subjectTensile stress-
dc.subjectX-ray imaging-
dc.subjectMachine learning-
dc.titleImage-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning-
dc.typeArticle-
dc.identifier.emailWu, W: weiwenwu@hku.hk-
dc.identifier.emailVardhanabhuti, V: varv@hku.hk-
dc.identifier.authorityVardhanabhuti, V=rp01900-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TRPMS.2020.2997880-
dc.identifier.hkuros310388-
dc.identifier.volumeEpub 2020-05-26-
dc.identifier.spage1-
dc.identifier.epage1-
dc.publisher.placeUnited States-

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