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Article: Accelerated MR diffusion tensor imaging using distributed compressed sensing

TitleAccelerated MR diffusion tensor imaging using distributed compressed sensing
Authors
KeywordsDiffusion tensor imaging
Distributed compressed sensing
Fast imaging
Joint sparsity constraint
Issue Date2014
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0740-3194/
Citation
Magnetic Resonance in Medicine, 2014, v. 71 n. 2, p. 763-772 How to Cite?
AbstractPurpose: Diffusion tensor imaging (DTI) is known to suffer from long acquisition time in the orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data acquisition is highly desirable. In this article, the feasibility and efficacy of distributed compressed sensing to fast DTI is investigated by exploiting the joint sparsity prior in diffusion‐weighted images. Methods: Fully sampled DTI datasets were obtained from both simulated phantom and experimental heart sample, with diffusion gradient applied in six directions. The k‐space data were undersampled retrospectively with acceleration factors from 2 to 6. Diffusion‐weighted images were reconstructed by solving an l2‐l1 norm minimization problem. Reconstruction performance with varied signal‐to‐noise ratio and acceleration factors were evaluated by root‐mean‐square error and maps of reconstructed DTI indices. Results: Superiority of distributed compressed sensing over basic compressed sensing was confirmed with simulation, and the reconstruction accuracy was influenced by signal‐to‐noise ratio and acceleration factors. Experimental results demonstrate that DTI indices including fractional anisotropy, mean diffusivities, and orientation of primary eigenvector can be obtained with high accuracy at acceleration factors up to 4. Conclusion: Distributed compressed sensing is shown to be able to accelerate DTI and may be used to reduce DTI acquisition time practically. Magn Reson Med 71:763–772, 2014. © 2013 Wiley Periodicals, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/216964
ISSN
2019 Impact Factor: 3.635
2015 SCImago Journal Rankings: 2.197
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, Y-
dc.contributor.authorZhu, YJ-
dc.contributor.authorTang, QY-
dc.contributor.authorZou, C-
dc.contributor.authorLiu, W-
dc.contributor.authorDai, RB-
dc.contributor.authorLiu, X-
dc.contributor.authorWu, EX-
dc.contributor.authorYing, L-
dc.contributor.authorLiang, D-
dc.date.accessioned2015-09-18T05:44:24Z-
dc.date.available2015-09-18T05:44:24Z-
dc.date.issued2014-
dc.identifier.citationMagnetic Resonance in Medicine, 2014, v. 71 n. 2, p. 763-772-
dc.identifier.issn0740-3194-
dc.identifier.urihttp://hdl.handle.net/10722/216964-
dc.description.abstractPurpose: Diffusion tensor imaging (DTI) is known to suffer from long acquisition time in the orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data acquisition is highly desirable. In this article, the feasibility and efficacy of distributed compressed sensing to fast DTI is investigated by exploiting the joint sparsity prior in diffusion‐weighted images. Methods: Fully sampled DTI datasets were obtained from both simulated phantom and experimental heart sample, with diffusion gradient applied in six directions. The k‐space data were undersampled retrospectively with acceleration factors from 2 to 6. Diffusion‐weighted images were reconstructed by solving an l2‐l1 norm minimization problem. Reconstruction performance with varied signal‐to‐noise ratio and acceleration factors were evaluated by root‐mean‐square error and maps of reconstructed DTI indices. Results: Superiority of distributed compressed sensing over basic compressed sensing was confirmed with simulation, and the reconstruction accuracy was influenced by signal‐to‐noise ratio and acceleration factors. Experimental results demonstrate that DTI indices including fractional anisotropy, mean diffusivities, and orientation of primary eigenvector can be obtained with high accuracy at acceleration factors up to 4. Conclusion: Distributed compressed sensing is shown to be able to accelerate DTI and may be used to reduce DTI acquisition time practically. Magn Reson Med 71:763–772, 2014. © 2013 Wiley Periodicals, Inc.-
dc.languageeng-
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0740-3194/-
dc.relation.ispartofMagnetic Resonance in Medicine-
dc.subjectDiffusion tensor imaging-
dc.subjectDistributed compressed sensing-
dc.subjectFast imaging-
dc.subjectJoint sparsity constraint-
dc.titleAccelerated MR diffusion tensor imaging using distributed compressed sensing-
dc.typeArticle-
dc.identifier.emailWu, EX: ewu1@hkucc.hku.hk-
dc.identifier.authorityWu, EX=rp00193-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1002/mrm.24721.-
dc.identifier.pmid23494999-
dc.identifier.scopuseid_2-s2.0-84892415280-
dc.identifier.hkuros251673-
dc.identifier.volume71-
dc.identifier.issue2-
dc.identifier.spage763-
dc.identifier.epage772-
dc.identifier.isiWOS:000330769700032-
dc.publisher.placeUnited States-
dc.identifier.issnl0740-3194-

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