Source:http://linkedlifedata.com/resource/pubmed/id/21292013
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
3
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| pubmed:dateCreated |
2011-5-2
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| pubmed:abstractText |
High angular resolution diffusion imaging (HARDI) has become an important technique for imaging complex oriented structures in the brain and other anatomical tissues. This has motivated the recent development of several methods for computing the orientation probability density function (PDF) at each voxel. However, much less work has been done on developing techniques for filtering, interpolation, averaging and principal geodesic analysis of orientation PDF fields. In this paper, we present a Riemannian framework for performing such operations. The proposed framework does not require that the orientation PDFs be represented by any fixed parameterization, such as a mixture of von Mises-Fisher distributions or a spherical harmonic expansion. Instead, we use a nonparametric representation of the orientation PDF. We exploit the fact that under the square-root re-parameterization, the space of orientation PDFs forms a Riemannian manifold: the positive orthant of the unit Hilbert sphere. We show that various orientation PDF processing operations, such as filtering, interpolation, averaging and principal geodesic analysis, may be posed as optimization problems on the Hilbert sphere, and can be solved using Riemannian gradient descent. We illustrate these concepts with numerous experiments on synthetic, phantom and real datasets. We show their application to studying left/right brain asymmetries.
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| pubmed:grant |
http://linkedlifedata.com/resource/pubmed/grant/P30 NS057091,
http://linkedlifedata.com/resource/pubmed/grant/P41 RR008079,
http://linkedlifedata.com/resource/pubmed/grant/R01 EB007813,
http://linkedlifedata.com/resource/pubmed/grant/R01 EB007813-02,
http://linkedlifedata.com/resource/pubmed/grant/R01 EB008432,
http://linkedlifedata.com/resource/pubmed/grant/R01 EB008432-02,
http://linkedlifedata.com/resource/pubmed/grant/R01 HD050735,
http://linkedlifedata.com/resource/pubmed/grant/R01 HD050735-02
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:month |
Jun
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| pubmed:issn |
1095-9572
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| pubmed:author | |
| pubmed:copyrightInfo |
Copyright © 2011 Elsevier Inc. All rights reserved.
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| pubmed:issnType |
Electronic
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| pubmed:day |
1
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| pubmed:volume |
56
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
1181-201
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| pubmed:dateRevised |
2011-8-1
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| pubmed:meshHeading |
pubmed-meshheading:21292013-Algorithms,
pubmed-meshheading:21292013-Animals,
pubmed-meshheading:21292013-Anisotropy,
pubmed-meshheading:21292013-Brain,
pubmed-meshheading:21292013-Data Interpretation, Statistical,
pubmed-meshheading:21292013-Diffusion Magnetic Resonance Imaging,
pubmed-meshheading:21292013-Functional Laterality,
pubmed-meshheading:21292013-Humans,
pubmed-meshheading:21292013-Image Enhancement,
pubmed-meshheading:21292013-Image Processing, Computer-Assisted,
pubmed-meshheading:21292013-Linear Models,
pubmed-meshheading:21292013-Models, Statistical,
pubmed-meshheading:21292013-Nerve Fibers,
pubmed-meshheading:21292013-Phantoms, Imaging,
pubmed-meshheading:21292013-Population,
pubmed-meshheading:21292013-Principal Component Analysis,
pubmed-meshheading:21292013-Rats,
pubmed-meshheading:21292013-Rats, Sprague-Dawley
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| pubmed:year |
2011
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| pubmed:articleTitle |
A nonparametric Riemannian framework for processing high angular resolution diffusion images and its applications to ODF-based morphometry.
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| pubmed:affiliation |
Department of Mathematics, National University of Singapore, Singapore. agoh@nus.edu.sg
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| pubmed:publicationType |
Journal Article,
Comparative Study,
Research Support, U.S. Gov't, Non-P.H.S.,
Research Support, Non-U.S. Gov't,
Research Support, N.I.H., Extramural
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