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Article: Diffusion kurtosis imaging based on adaptive spherical integral

TitleDiffusion kurtosis imaging based on adaptive spherical integral
Authors
Liu, YChen, LYu, Y
KeywordsAdaptive spherical integral
Kurtosis imaging
MRI
Optimization
Medical imaging
Issue Date2011
PublisherIEEE. The Journal's website is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=97
Citation
IEEE Signal Processing Letters, 2011, v. 18 n. 4, p. 243-246 How to Cite?
DOI: http://dx.doi.org/10.1109/LSP.2011.2113339
AbstractDiffusion kurtosis imaging (DKI) is a recent approach in medical engineering that has potential value for both neurological diseases and basic neuroscience research. In this letter, we develop a robust method based on adaptive spherical integral that can compute kurtosis based quantities more precisely and efficiently. Our method integrates spherical trigonometry with a recursive computational scheme to make numerical estimations in kurtosis imaging convergent. Our algorithm improves the efficiency of computing integral invariants based on reconstructed diffusion kurtosis tensors and makes DKI better prepared for further clinical applications. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/140807
ISSN
1070-9908
2023 Impact Factor: 3.2
2023 SCImago Journal Rankings: 1.271
ISI Accession Number ID
WOS:000287653000005
Funding AgencyGrant Number
National Science FoundationIIS 09-14631
China Scholarship Council
Funding Information:

Manuscript received December 04, 2010; revised January 25, 2011; accepted January 28, 2011. Date of publication February 10, 2011; date of current version February 17, 2011. This work was supported in part by National Science Foundation (IIS 09-14631) and China Scholarship Council. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Yong Man Ro.

References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorLiu, Yen_HK
dc.contributor.authorChen, Len_HK
dc.contributor.authorYu, Yen_HK
dc.date.accessioned2011-09-23T06:19:33Z-
dc.date.available2011-09-23T06:19:33Z-
dc.date.issued2011en_HK
dc.identifier.citationIEEE Signal Processing Letters, 2011, v. 18 n. 4, p. 243-246en_HK
dc.identifier.issn1070-9908en_HK
dc.identifier.urihttp://hdl.handle.net/10722/140807-
dc.description.abstractDiffusion kurtosis imaging (DKI) is a recent approach in medical engineering that has potential value for both neurological diseases and basic neuroscience research. In this letter, we develop a robust method based on adaptive spherical integral that can compute kurtosis based quantities more precisely and efficiently. Our method integrates spherical trigonometry with a recursive computational scheme to make numerical estimations in kurtosis imaging convergent. Our algorithm improves the efficiency of computing integral invariants based on reconstructed diffusion kurtosis tensors and makes DKI better prepared for further clinical applications. © 2011 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's website is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=97-
dc.relation.ispartofIEEE Signal Processing Lettersen_HK
dc.rightsIEEE Signal Processing Letters. Copyright © IEEE.-
dc.subjectAdaptive spherical integralen_HK
dc.subjectKurtosis imagingen_HK
dc.subjectMRIen_HK
dc.subjectOptimizationen_HK
dc.subjectMedical imaging-
dc.titleDiffusion kurtosis imaging based on adaptive spherical integralen_HK
dc.typeArticleen_HK
dc.identifier.emailYu, Y: yizhouy@hku.hken_HK
dc.identifier.authorityYu, Y=rp01415en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/LSP.2011.2113339en_HK
dc.identifier.scopuseid_2-s2.0-79951864504en_HK
dc.identifier.hkuros194314en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79951864504&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume18en_HK
dc.identifier.issue4en_HK
dc.identifier.spage243en_HK
dc.identifier.epage246en_HK
dc.identifier.isiWOS:000287653000005-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYu, Y=8554163500en_HK
dc.identifier.scopusauthoridChen, L=14055650900en_HK
dc.identifier.scopusauthoridLiu, Y=36844116200en_HK
dc.identifier.issnl1070-9908-

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