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Article: Discrepancy-based adaptive regularization for GRAPPA reconstruction

TitleDiscrepancy-based adaptive regularization for GRAPPA reconstruction
Authors
KeywordsDiscrepancy principle
GRAPPA
L-curve
Parallel imaging
Regularization
Issue Date2006
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/1053-1807/
Citation
Journal Of Magnetic Resonance Imaging, 2006, v. 24 n. 1, p. 248-255 How to Cite?
AbstractPurpose: To develop a novel regularization method for GRAPPA by which the regularization parameters can be optimally and adaptively chosen. Materials and Methods: In the fit procedures in GRAPPA, the discrepancy principle, which chooses the regularization parameter based on a priori information about the noise level in the autocalibrating signals (ACS), is used with the truncated singular value decomposition (TSVD) regularization and the Tikhonov regularization, and its performance is compared with the singular value (SV) threshold method and the L-curve method, respectively by axial and sagittal head imaging experiments. Results: In both axial and sagittal reconstructions, normal GRAPPA reconstruction results exhibit a relatively high level of noise. With discrepancy-based choices of parameters, regularization can improve the signal-to-noise ratio (SNR) with only a very modest increase in aliasing artifacts. The L-curve method in all of the reconstructions leads to overregularization, which causes severe residual aliasing artifacts. The 10% SV threshold method yields good overall image quality in the axial case, but in the sagittal case it also leads to an obvious increase in aliasing artifacts. Conclusion: Neither a fixed SV threshold nor the L-curve are robust means of choosing the appropriate parameters in GRAPPA reconstruction. However, with the discrepancy-based parameter-choice strategy, adaptively regularized GRAPPA can be used to automatically choose nearly optimal parameters for reconstruction and achieve an excellent compromise between SNR and artifacts. © 2006 Wiley-Liss, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/73910
ISSN
2015 Impact Factor: 3.25
2015 SCImago Journal Rankings: 1.683
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorQu, Pen_HK
dc.contributor.authorWang, Cen_HK
dc.contributor.authorShen, GXen_HK
dc.date.accessioned2010-09-06T06:55:57Z-
dc.date.available2010-09-06T06:55:57Z-
dc.date.issued2006en_HK
dc.identifier.citationJournal Of Magnetic Resonance Imaging, 2006, v. 24 n. 1, p. 248-255en_HK
dc.identifier.issn1053-1807en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73910-
dc.description.abstractPurpose: To develop a novel regularization method for GRAPPA by which the regularization parameters can be optimally and adaptively chosen. Materials and Methods: In the fit procedures in GRAPPA, the discrepancy principle, which chooses the regularization parameter based on a priori information about the noise level in the autocalibrating signals (ACS), is used with the truncated singular value decomposition (TSVD) regularization and the Tikhonov regularization, and its performance is compared with the singular value (SV) threshold method and the L-curve method, respectively by axial and sagittal head imaging experiments. Results: In both axial and sagittal reconstructions, normal GRAPPA reconstruction results exhibit a relatively high level of noise. With discrepancy-based choices of parameters, regularization can improve the signal-to-noise ratio (SNR) with only a very modest increase in aliasing artifacts. The L-curve method in all of the reconstructions leads to overregularization, which causes severe residual aliasing artifacts. The 10% SV threshold method yields good overall image quality in the axial case, but in the sagittal case it also leads to an obvious increase in aliasing artifacts. Conclusion: Neither a fixed SV threshold nor the L-curve are robust means of choosing the appropriate parameters in GRAPPA reconstruction. However, with the discrepancy-based parameter-choice strategy, adaptively regularized GRAPPA can be used to automatically choose nearly optimal parameters for reconstruction and achieve an excellent compromise between SNR and artifacts. © 2006 Wiley-Liss, Inc.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/1053-1807/en_HK
dc.relation.ispartofJournal of Magnetic Resonance Imagingen_HK
dc.rightsJournal of Magnetic Resonance Imaging. Copyright © John Wiley & Sons, Inc.en_HK
dc.subjectDiscrepancy principleen_HK
dc.subjectGRAPPAen_HK
dc.subjectL-curveen_HK
dc.subjectParallel imagingen_HK
dc.subjectRegularizationen_HK
dc.titleDiscrepancy-based adaptive regularization for GRAPPA reconstructionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1053-1807&volume=24 &spage=248&epage=255&date=2006&atitle=Discrepancy-based+Adaptive+Regularization+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.doi10.1002/jmri.20620en_HK
dc.identifier.pmid16758468-
dc.identifier.scopuseid_2-s2.0-33745686035en_HK
dc.identifier.hkuros137328en_HK
dc.identifier.hkuros121264-
dc.identifier.hkuros129120-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33745686035&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume24en_HK
dc.identifier.issue1en_HK
dc.identifier.spage248en_HK
dc.identifier.epage255en_HK
dc.identifier.isiWOS:000238894900033-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridQu, P=36838745800en_HK
dc.identifier.scopusauthoridWang, C=49764173800en_HK
dc.identifier.scopusauthoridShen, GX=7401967224en_HK

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