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Article: Tailored utilization of acquired k-space points for GRAPPA reconstruction

TitleTailored utilization of acquired k-space points for GRAPPA reconstruction
Authors
KeywordsGRAPPA
Image reconstruction
Parallel imaging
RF coil array
SMASH
Issue Date2005
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/yjmre
Citation
Journal Of Magnetic Resonance, 2005, v. 174 n. 1, p. 60-67 How to Cite?
AbstractThe generalized auto-calibrating partially parallel acquisition (GRAPPA) is an auto-calibrating parallel imaging technique which incorporates multiple blocks of data to derive the missing signals. In the original GRAPPA reconstruction algorithm only the data points in phase encoding direction are incorporated to reconstruct missing points in k-space. It has been recognized that this scheme can be extended so that data points in readout direction are also utilized and the points are selected based on a k-space locality criterion. In this study, an automatic subset selection strategy is proposed which can provide a tailored selection of source points for reconstruction. This novel approach extracts a subset of signal points corresponding to the most linearly independent base vectors in the coefficient matrix of fit, effectively preventing incorporating redundant signals which only bring noise into reconstruction with little contribution to the exactness of fit. Also, subset selection in this way has a regularization effect since the vectors corresponding to the smallest singular values are eliminated and consequently the condition of the reconstruction is improved. Phantom and in vivo MRI experiments demonstrate that this subset selection strategy can effectively improve SNR and reduce residual artifacts for GRAPPA reconstruction. Published by Elsevier Inc.
Persistent Identifierhttp://hdl.handle.net/10722/73823
ISSN
2015 Impact Factor: 2.889
2015 SCImago Journal Rankings: 1.157
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorQu, Pen_HK
dc.contributor.authorShen, GXen_HK
dc.contributor.authorWang, Cen_HK
dc.contributor.authorWu, Ben_HK
dc.contributor.authorYuan, Jen_HK
dc.date.accessioned2010-09-06T06:55:06Z-
dc.date.available2010-09-06T06:55:06Z-
dc.date.issued2005en_HK
dc.identifier.citationJournal Of Magnetic Resonance, 2005, v. 174 n. 1, p. 60-67en_HK
dc.identifier.issn1090-7807en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73823-
dc.description.abstractThe generalized auto-calibrating partially parallel acquisition (GRAPPA) is an auto-calibrating parallel imaging technique which incorporates multiple blocks of data to derive the missing signals. In the original GRAPPA reconstruction algorithm only the data points in phase encoding direction are incorporated to reconstruct missing points in k-space. It has been recognized that this scheme can be extended so that data points in readout direction are also utilized and the points are selected based on a k-space locality criterion. In this study, an automatic subset selection strategy is proposed which can provide a tailored selection of source points for reconstruction. This novel approach extracts a subset of signal points corresponding to the most linearly independent base vectors in the coefficient matrix of fit, effectively preventing incorporating redundant signals which only bring noise into reconstruction with little contribution to the exactness of fit. Also, subset selection in this way has a regularization effect since the vectors corresponding to the smallest singular values are eliminated and consequently the condition of the reconstruction is improved. Phantom and in vivo MRI experiments demonstrate that this subset selection strategy can effectively improve SNR and reduce residual artifacts for GRAPPA reconstruction. Published by Elsevier Inc.en_HK
dc.languageengen_HK
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/yjmreen_HK
dc.relation.ispartofJournal of Magnetic Resonanceen_HK
dc.subjectGRAPPAen_HK
dc.subjectImage reconstructionen_HK
dc.subjectParallel imagingen_HK
dc.subjectRF coil arrayen_HK
dc.subjectSMASHen_HK
dc.titleTailored utilization of acquired k-space points for GRAPPA reconstructionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1090-7807&volume=174&spage=60&epage=67&date=2005&atitle=Tailored+Utilization+of+Acquired+k-space+Points+for+GRAPPA+Reconstructionen_HK
dc.identifier.emailShen, GX: gxshen@eee.hku.hken_HK
dc.identifier.authorityShen, GX=rp00166en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jmr.2005.01.015en_HK
dc.identifier.pmid15809173-
dc.identifier.scopuseid_2-s2.0-15844396465en_HK
dc.identifier.hkuros121240en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-15844396465&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume174en_HK
dc.identifier.issue1en_HK
dc.identifier.spage60en_HK
dc.identifier.epage67en_HK
dc.identifier.isiWOS:000228938700006-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridQu, P=36838745800en_HK
dc.identifier.scopusauthoridShen, GX=7401967224en_HK
dc.identifier.scopusauthoridWang, C=23010721800en_HK
dc.identifier.scopusauthoridWu, B=7403590770en_HK
dc.identifier.scopusauthoridYuan, J=35788305600en_HK
dc.identifier.citeulike3339393-

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