File Download
 
Links for fulltext
(May Require Subscription)
 
Supplementary

Conference Paper: Reconstructing diffusion kurtosis tensors from sparse noisy measurements
  • Basic View
  • Metadata View
  • XML View
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 FieldValue
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
 
<?xml encoding="utf-8" version="1.0"?>
<item><contributor.author>Liu, Y</contributor.author>
<contributor.author>Wei, S</contributor.author>
<contributor.author>Jiang, Q</contributor.author>
<contributor.author>Yu, Y</contributor.author>
<date.accessioned>2011-09-23T06:04:32Z</date.accessioned>
<date.available>2011-09-23T06:04:32Z</date.available>
<date.issued>2010</date.issued>
<identifier.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</identifier.citation>
<identifier.issn>1522-4880</identifier.issn>
<identifier.uri>http://hdl.handle.net/10722/140000</identifier.uri>
<description.abstract>Diffusion 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. &#169; 2010 IEEE.</description.abstract>
<language>eng</language>
<publisher>IEEE. The Journal&apos;s web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349</publisher>
<relation.ispartof>Proceedings of the International Conference on Image Processing, ICIP 2010</relation.ispartof>
<rights>International Conference on Image Processing Proceedings. Copyright &#169; IEEE.</rights>
<rights>Creative Commons: Attribution 3.0 Hong Kong License</rights>
<rights>&#169;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.</rights>
<subject>Denoising</subject>
<subject>Kurtosis tensors</subject>
<subject>Model reconstruction</subject>
<subject>MRI</subject>
<subject>Optimization</subject>
<title>Reconstructing diffusion kurtosis tensors from sparse noisy measurements</title>
<type>Conference_Paper</type>
<description.nature>published_or_final_version</description.nature>
<identifier.doi>10.1109/ICIP.2010.5649554</identifier.doi>
<identifier.scopus>eid_2-s2.0-78651061449</identifier.scopus>
<identifier.hkuros>194321</identifier.hkuros>
<relation.references>http://www.scopus.com/mlt/select.url?eid=2-s2.0-78651061449&amp;selection=ref&amp;src=s&amp;origin=recordpage</relation.references>
<identifier.spage>4185</identifier.spage>
<identifier.epage>4188</identifier.epage>
<identifier.isi>WOS:000287728004059</identifier.isi>
<publisher.place>United States</publisher.place>
<description.other>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</description.other>
<bitstream.url>http://hub.hku.hk/bitstream/10722/140000/1/Content.pdf</bitstream.url>
</item>
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