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Conference Paper: Reconstructing diffusion kurtosis tensors from sparse noisy measurements

TitleReconstructing diffusion kurtosis tensors from sparse noisy measurements
Authors
KeywordsDenoising
Kurtosis tensors
Model reconstruction
MRI
Optimization
Issue Date2010
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349
Citation
The 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 4185-4188 How to Cite?
AbstractDiffusion kurtosis imaging (DKI) is a recent MRI based method that can quantify deviation from Gaussian behavior using a kurtosis tensor. DKI has potential value for the assessment of neurologic diseases. Existing techniques for diffusion kurtosis imaging typically need to capture hundreds of MRI images, which is not clinically feasible on human subjects. In this paper, we develop robust denoising and model fitting methods that make it possible to accurately reconstruct a kurtosis tensor from 75 or less noisy measurements. Our denoising method is based on subspace learning for multi-dimensional signals and our model fitting technique uses iterative reweighting to effectively discount the influences of outliers. The total data acquisition time thus drops significantly, making diffusion kurtosis imaging feasible for many clinical applications involving human subjects. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/140000
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLiu, Yen_HK
dc.contributor.authorWei, Sen_HK
dc.contributor.authorJiang, Qen_HK
dc.contributor.authorYu, Yen_HK
dc.date.accessioned2011-09-23T06:04:32Z-
dc.date.available2011-09-23T06:04:32Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 4185-4188en_HK
dc.identifier.issn1522-4880en_HK
dc.identifier.urihttp://hdl.handle.net/10722/140000-
dc.description.abstractDiffusion kurtosis imaging (DKI) is a recent MRI based method that can quantify deviation from Gaussian behavior using a kurtosis tensor. DKI has potential value for the assessment of neurologic diseases. Existing techniques for diffusion kurtosis imaging typically need to capture hundreds of MRI images, which is not clinically feasible on human subjects. In this paper, we develop robust denoising and model fitting methods that make it possible to accurately reconstruct a kurtosis tensor from 75 or less noisy measurements. Our denoising method is based on subspace learning for multi-dimensional signals and our model fitting technique uses iterative reweighting to effectively discount the influences of outliers. The total data acquisition time thus drops significantly, making diffusion kurtosis imaging feasible for many clinical applications involving human subjects. © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349en_HK
dc.relation.ispartofProceedings of the International Conference on Image Processing, ICIP 2010en_HK
dc.rightsInternational Conference on Image Processing Proceedings. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectDenoisingen_HK
dc.subjectKurtosis tensorsen_HK
dc.subjectModel reconstructionen_HK
dc.subjectMRIen_HK
dc.subjectOptimizationen_HK
dc.titleReconstructing diffusion kurtosis tensors from sparse noisy measurementsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYu, Y:yzyu@cs.hku.hken_HK
dc.identifier.authorityYu, Y=rp01415en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICIP.2010.5649554en_HK
dc.identifier.scopuseid_2-s2.0-78651061449en_HK
dc.identifier.hkuros194321en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78651061449&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage4185en_HK
dc.identifier.epage4188en_HK
dc.identifier.isiWOS:000287728004059-
dc.publisher.placeUnited Statesen_HK
dc.description.otherThe 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 4185-4188-
dc.identifier.scopusauthoridLiu, Y=36844116200en_HK
dc.identifier.scopusauthoridWei, S=36845050600en_HK
dc.identifier.scopusauthoridJiang, Q=13905424700en_HK
dc.identifier.scopusauthoridYu, Y=8554163500en_HK

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