Fast single image super-resolution using estimated low-frequency k-space data in MRI.
Identifieur interne : 000C36 ( PubMed/Checkpoint ); précédent : 000C35; suivant : 000C37Fast single image super-resolution using estimated low-frequency k-space data in MRI.
Auteurs : Jianhua Luo [République populaire de Chine] ; Zhiying Mou [République populaire de Chine] ; Binjie Qin [République populaire de Chine] ; Wanqing Li [Australie] ; Feng Yang [République populaire de Chine] ; Marc Robini [France] ; Yuemin Zhu [France]Source :
- Magnetic resonance imaging [ 1873-5894 ] ; 2017.
Abstract
Single image super-resolution (SR) is highly desired in many fields but obtaining it is often technically limited in practice. The purpose of this study was to propose a simple, rapid and robust single image SR method in magnetic resonance (MR) imaging (MRI).
DOI: 10.1016/j.mri.2017.03.008
PubMed: 28366758
Affiliations:
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Electronic address: bjqin@sjtu.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240</wicri:regionArea><wicri:noRegion>Shanghai 200240</wicri:noRegion></affiliation></author><author><name sortKey="Li, Wanqing" sort="Li, Wanqing" uniqKey="Li W" first="Wanqing" last="Li">Wanqing Li</name><affiliation wicri:level="1"><nlm:affiliation>School of Computer Science and Software Engineering, University of Wollongong, NSW 2522, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>School of Computer Science and Software Engineering, University of Wollongong, NSW 2522</wicri:regionArea><wicri:noRegion>NSW 2522</wicri:noRegion></affiliation></author><author><name sortKey="Yang, Feng" sort="Yang, Feng" uniqKey="Yang F" first="Feng" last="Yang">Feng Yang</name><affiliation wicri:level="1"><nlm:affiliation>School of Computer and Information Technology, Beijing Jiao Tong University, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Computer and Information Technology, Beijing Jiao Tong University</wicri:regionArea><wicri:noRegion>Beijing Jiao Tong University</wicri:noRegion></affiliation></author><author><name sortKey="Robini, Marc" sort="Robini, Marc" uniqKey="Robini M" first="Marc" last="Robini">Marc Robini</name><affiliation wicri:level="1"><nlm:affiliation>University of Lyon; CNRS UMR 5220; Inserm U1206; INSA Lyon, Creatis, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>University of Lyon; CNRS UMR 5220; Inserm U1206; INSA Lyon, Creatis</wicri:regionArea><wicri:noRegion>Creatis</wicri:noRegion><wicri:noRegion>Creatis</wicri:noRegion></affiliation></author><author><name sortKey="Zhu, Yuemin" sort="Zhu, Yuemin" uniqKey="Zhu Y" first="Yuemin" last="Zhu">Yuemin Zhu</name><affiliation wicri:level="1"><nlm:affiliation>University of Lyon; CNRS UMR 5220; Inserm U1206; INSA Lyon, Creatis, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>University of Lyon; CNRS UMR 5220; Inserm U1206; INSA Lyon, Creatis</wicri:regionArea><wicri:noRegion>Creatis</wicri:noRegion><wicri:noRegion>Creatis</wicri:noRegion></affiliation></author></analytic><series><title level="j">Magnetic resonance imaging</title><idno type="eISSN">1873-5894</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Single image super-resolution (SR) is highly desired in many fields but obtaining it is often technically limited in practice. The purpose of this study was to propose a simple, rapid and robust single image SR method in magnetic resonance (MR) imaging (MRI).</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">28366758</PMID><DateCreated><Year>2017</Year><Month>04</Month><Day>03</Day></DateCreated><DateRevised><Year>2017</Year><Month>06</Month><Day>03</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1873-5894</ISSN><JournalIssue CitedMedium="Internet"><Volume>40</Volume><PubDate><Year>2017</Year><Month>Jul</Month></PubDate></JournalIssue><Title>Magnetic resonance imaging</Title><ISOAbbreviation>Magn Reson Imaging</ISOAbbreviation></Journal><ArticleTitle>Fast single image super-resolution using estimated low-frequency k-space data in MRI.</ArticleTitle><Pagination><MedlinePgn>1-11</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0730-725X(17)30060-7</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.mri.2017.03.008</ELocationID><Abstract><AbstractText Label="PURPOSE" NlmCategory="OBJECTIVE">Single image super-resolution (SR) is highly desired in many fields but obtaining it is often technically limited in practice. The purpose of this study was to propose a simple, rapid and robust single image SR method in magnetic resonance (MR) imaging (MRI).</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">The idea is based on the mathematical formulation of the intrinsic link in k-space between a given (modulus) low-resolution (LR) image and the desired SR image. The method consists of two steps: 1) estimating the low-frequency k-space data of the desired SR image from a single LR image; 2) reconstructing the SR image using the estimated low-frequency and zero-filled high-frequency k-space data. The method was evaluated on digital phantom images, physical phantom MR images and real brain MR images, and compared with existing SR methods.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The proposed SR method exhibited a good robustness by reaching a clearly higher PSNR (25.77dB) and SSIM (0.991) averaged over different noise levels in comparison with existing edge-guided nonlinear interpolation (EGNI) (PSNR=23.78dB, SSIM=0.983), zero-filling (ZF) (PSNR=24.09dB, SSIM=0.985) and total variation (TV) (PSNR=24.54dB, SSIM=0.987) methods while presenting the same order of computation time as the ZF method but being much faster than the EGNI or TV method. The average PSNR or SSIM over different slice images of the proposed method (PSNR=26.33 dB or SSIM=0.955) was also higher than the EGNI (PSNR=25.07dB or SSIM=0.952), ZF (PSNR=24.97dB or SSIM=0.950) and TV (PSNR=25.70dB or SSIM=0.953) methods, demonstrating its good robustness to variation in anatomical structure of the images. Meanwhile, the proposed method always produced less ringing artifacts than the ZF method, gave a clearer image than the EGNI method, and did not exhibit any blocking effect presented in the TV method. In addition, the proposed method yielded the highest spatial consistency in the inter-slice dimension among the four methods.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">This study proposed a fast, robust and efficient single image SR method with high spatial consistency in the inter-slice dimension for clinical MR images by estimating the low-frequency k-space data of the desired SR image from a single spatial modulus LR image.</AbstractText><CopyrightInformation>Copyright © 2017 Elsevier Inc. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Luo</LastName><ForeName>Jianhua</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>School of Aeronautics and Astronautics, Shanghai Jiao Tong University, 200240, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mou</LastName><ForeName>Zhiying</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>China National Aeronautical Radio Electronics Research Institute, Shanghai 200233, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Qin</LastName><ForeName>Binjie</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: bjqin@sjtu.edu.cn.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Wanqing</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>School of Computer Science and Software Engineering, University of Wollongong, NSW 2522, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Feng</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>School of Computer and Information Technology, Beijing Jiao Tong University, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Robini</LastName><ForeName>Marc</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>University of Lyon; CNRS UMR 5220; Inserm U1206; INSA Lyon, Creatis, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhu</LastName><ForeName>Yuemin</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>University of Lyon; CNRS UMR 5220; Inserm U1206; INSA Lyon, Creatis, France.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>03</Month><Day>31</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Magn Reson Imaging</MedlineTA><NlmUniqueID>8214883</NlmUniqueID><ISSNLinking>0730-725X</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Image interpolation</Keyword><Keyword MajorTopicYN="N">Magnetic resonance imaging</Keyword><Keyword MajorTopicYN="N">Super-resolution</Keyword><Keyword MajorTopicYN="N">k-Space data</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2017</Year><Month>01</Month><Day>17</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2017</Year><Month>03</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2017</Year><Month>03</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2017</Year><Month>4</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2017</Year><Month>4</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2017</Year><Month>4</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">28366758</ArticleId><ArticleId IdType="pii">S0730-725X(17)30060-7</ArticleId><ArticleId IdType="doi">10.1016/j.mri.2017.03.008</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Australie</li><li>France</li><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Luo, Jianhua" sort="Luo, Jianhua" uniqKey="Luo J" first="Jianhua" last="Luo">Jianhua Luo</name></noRegion><name sortKey="Mou, Zhiying" sort="Mou, Zhiying" uniqKey="Mou Z" first="Zhiying" last="Mou">Zhiying Mou</name><name sortKey="Qin, Binjie" sort="Qin, Binjie" uniqKey="Qin B" first="Binjie" last="Qin">Binjie Qin</name><name sortKey="Yang, Feng" sort="Yang, Feng" uniqKey="Yang F" first="Feng" last="Yang">Feng Yang</name></country><country name="Australie"><noRegion><name sortKey="Li, Wanqing" sort="Li, Wanqing" uniqKey="Li W" first="Wanqing" last="Li">Wanqing Li</name></noRegion></country><country name="France"><noRegion><name sortKey="Robini, Marc" sort="Robini, Marc" uniqKey="Robini M" first="Marc" last="Robini">Marc Robini</name></noRegion><name sortKey="Zhu, Yuemin" sort="Zhu, Yuemin" uniqKey="Zhu Y" first="Yuemin" last="Zhu">Yuemin Zhu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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