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Article: A least squares quantization table method for direct reconstruction of MR images with non-Cartesian trajectory

TitleA least squares quantization table method for direct reconstruction of MR images with non-Cartesian trajectory
Authors
Issue Date2007
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/yjmre
Citation
Journal Of Magnetic Resonance, 2007, v. 188 n. 1, p. 141-150 How to Cite?
AbstractThe direct Fourier transform method is a straightforward solution with high accuracy for reconstructing magnetic resonance (MR) images from nonuniformly sampled k-space data, given that the optimal density compensation function is selected and the underlying magnetic field is sufficiently uniform. The computation however is very time-consuming, making it impractical especially for large-size images. In this paper, the least squares quantization table (LSQT) method is proposed to accelerate the direct Fourier transform computation, similar to the recently proposed methods such as using look-up table (LUT) or equal-phase-line (EPL). With LSQT, all the image pixels are first classified into several groups where the Lloyd-Max quantization scheme is used to ensure the minimal classification error. The representative value of each group is stored in a small-size LSQT in advance to reduce the computational load. The pixels in the same group receive the same contribution, which is calculated only once for each group instead of for each pixel, resulting in the reduction of computation because the number of groups is far smaller than the number of pixels. Finally, each image pixel is mapped into the nearest group and its representative value is used to reconstruct the image. The experimental results show that the LSQT method requires far smaller memory size than the LUT method and fewer multiplication operations than the LUT and EPL methods. Moreover, the LSQT method can perform large-size reconstructions that achieve comparable or higher accuracy as compared to the EPL and gridding methods when the appropriate parameters are given. The inherent parallel structure also makes the LSQT method easily adaptable to a multiprocessor system. © 2007 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/73553
ISSN
2015 Impact Factor: 2.889
2015 SCImago Journal Rankings: 1.157
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLiang, Den_HK
dc.contributor.authorLam, EYen_HK
dc.contributor.authorFung, GSKen_HK
dc.date.accessioned2010-09-06T06:52:27Z-
dc.date.available2010-09-06T06:52:27Z-
dc.date.issued2007en_HK
dc.identifier.citationJournal Of Magnetic Resonance, 2007, v. 188 n. 1, p. 141-150en_HK
dc.identifier.issn1090-7807en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73553-
dc.description.abstractThe direct Fourier transform method is a straightforward solution with high accuracy for reconstructing magnetic resonance (MR) images from nonuniformly sampled k-space data, given that the optimal density compensation function is selected and the underlying magnetic field is sufficiently uniform. The computation however is very time-consuming, making it impractical especially for large-size images. In this paper, the least squares quantization table (LSQT) method is proposed to accelerate the direct Fourier transform computation, similar to the recently proposed methods such as using look-up table (LUT) or equal-phase-line (EPL). With LSQT, all the image pixels are first classified into several groups where the Lloyd-Max quantization scheme is used to ensure the minimal classification error. The representative value of each group is stored in a small-size LSQT in advance to reduce the computational load. The pixels in the same group receive the same contribution, which is calculated only once for each group instead of for each pixel, resulting in the reduction of computation because the number of groups is far smaller than the number of pixels. Finally, each image pixel is mapped into the nearest group and its representative value is used to reconstruct the image. The experimental results show that the LSQT method requires far smaller memory size than the LUT method and fewer multiplication operations than the LUT and EPL methods. Moreover, the LSQT method can perform large-size reconstructions that achieve comparable or higher accuracy as compared to the EPL and gridding methods when the appropriate parameters are given. The inherent parallel structure also makes the LSQT method easily adaptable to a multiprocessor system. © 2007 Elsevier Inc. All rights reserved.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.subject.meshAlgorithmsen_HK
dc.subject.meshImage Processing, Computer-Assisteden_HK
dc.subject.meshMagnetic Resonance Imaging - methodsen_HK
dc.subject.meshPhantoms, Imagingen_HK
dc.titleA least squares quantization table method for direct reconstruction of MR images with non-Cartesian trajectoryen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1090-7807&volume=188&spage=141&epage=150&date=2007&atitle=A+least+squares+quantization+table+method+for+direct+reconstruction+of+MR+images+with+non-Cartesian+trajectoryen_HK
dc.identifier.emailLam, EY:elam@eee.hku.hken_HK
dc.identifier.authorityLam, EY=rp00131en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jmr.2007.06.013en_HK
dc.identifier.pmid17646119-
dc.identifier.scopuseid_2-s2.0-34548457275en_HK
dc.identifier.hkuros128974en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34548457275&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume188en_HK
dc.identifier.issue1en_HK
dc.identifier.spage141en_HK
dc.identifier.epage150en_HK
dc.identifier.isiWOS:000249750800015-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLiang, D=26643210600en_HK
dc.identifier.scopusauthoridLam, EY=7102890004en_HK
dc.identifier.scopusauthoridFung, GSK=7004213392en_HK

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