Conference Paper: Reconstructing diffusion kurtosis tensors from sparse noisy measurements

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TitleReconstructing diffusion kurtosis tensors from sparse noisy measurements
AuthorsLiu, Y2 3
Wei, S1
Jiang, Q4
Yu, Y3
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
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. 4185-4188 [How to Cite?]
DOI: http://dx.doi.org/10.1109/ICIP.2010.5649554
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.
ISSN1522-4880
DOIhttp://dx.doi.org/10.1109/ICIP.2010.5649554
ISI Accession Number IDWOS:000287728004059
ReferencesReferences in Scopus
DC Field
Value
dc.contributor.authorLiu, Y
dc.contributor.authorWei, S
dc.contributor.authorJiang, Q
dc.contributor.authorYu, Y
dc.date.accessioned2011-09-23T06:04:32Z
dc.date.available2011-09-23T06:04:32Z
dc.date.issued2010
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.
dc.description.naturepublished_or_final_version
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.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. 4185-4188 [How to Cite?]
DOI: http://dx.doi.org/10.1109/ICIP.2010.5649554
dc.identifier.doihttp://dx.doi.org/10.1109/ICIP.2010.5649554
dc.identifier.epage4188
dc.identifier.hkuros194321
dc.identifier.isiWOS:000287728004059
dc.identifier.issn1522-4880
dc.identifier.scopuseid_2-s2.0-78651061449
dc.identifier.spage4185
dc.identifier.urihttp://hdl.handle.net/10722/140000
dc.languageeng
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349
dc.publisher.placeUnited States
dc.relation.ispartofProceedings of the International Conference on Image Processing, ICIP 2010
dc.relation.referencesReferences in Scopus
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.subjectDenoising
dc.subjectKurtosis tensors
dc.subjectModel reconstruction
dc.subjectMRI
dc.subjectOptimization
dc.titleReconstructing diffusion kurtosis tensors from sparse noisy measurements
dc.typeConference_Paper
Author Affiliations
  1. Zhejiang University
  2. University of Electronic Science and Technology of China
  3. University of Illinois at Urbana-Champaign
  4. Henry Ford Hospital