Detection of Crossing White Matter Fibers with High-Order Tensors and Rank-k Decompositions

Handle URI:
http://hdl.handle.net/10754/597954
Title:
Detection of Crossing White Matter Fibers with High-Order Tensors and Rank-k Decompositions
Authors:
Jiao, Fangxiang; Gur, Yaniv; Johnson, Chris R.; Joshi, Sarang
Abstract:
Fundamental to high angular resolution diffusion imaging (HARDI), is the estimation of a positive-semidefinite orientation distribution function (ODF) and extracting the diffusion properties (e.g., fiber directions). In this work we show that these two goals can be achieved efficiently by using homogeneous polynomials to represent the ODF in the spherical deconvolution approach, as was proposed in the Cartesian Tensor-ODF (CT-ODF) formulation. Based on this formulation we first suggest an estimation method for positive-semidefinite ODF by solving a linear programming problem that does not require special parameterization of the ODF. We also propose a rank-k tensor decomposition, known as CP decomposition, to extract the fibers information from the estimated ODF. We show that this decomposition is superior to the fiber direction estimation via ODF maxima detection as it enables one to reach the full fiber separation resolution of the estimation technique. We assess the accuracy of this new framework by applying it to synthetic and experimentally obtained HARDI data. © 2011 Springer-Verlag.
Citation:
Jiao F, Gur Y, Johnson CR, Joshi S (2011) Detection of Crossing White Matter Fibers with High-Order Tensors and Rank-k Decompositions. Information Processing in Medical Imaging: 538–549. Available: http://dx.doi.org/10.1007/978-3-642-22092-0_44.
Publisher:
Springer Science + Business Media
Journal:
Information Processing in Medical Imaging
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
2011
DOI:
10.1007/978-3-642-22092-0_44
Type:
Book Chapter
ISSN:
0302-9743; 1611-3349
Sponsors:
The authors would like to thank Tamara G. Kolda of San-dia National Labs, Livermore, California, for useful discussions and comments re-garding this work. This research wasfunded by the NIH grants: 5R01EB007688,5R01HL092055, and by the NIH/NCRR Center for Integrative Biomedical Com-puting, P41-RR12553-10, Award No. KUS-C1-016-04, made by King AbdullahUniversity of Science and Technology (KAUST), and DOE SciDAC VACET.
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Full metadata record

DC FieldValue Language
dc.contributor.authorJiao, Fangxiangen
dc.contributor.authorGur, Yaniven
dc.contributor.authorJohnson, Chris R.en
dc.contributor.authorJoshi, Sarangen
dc.date.accessioned2016-02-25T12:59:28Zen
dc.date.available2016-02-25T12:59:28Zen
dc.date.issued2011en
dc.identifier.citationJiao F, Gur Y, Johnson CR, Joshi S (2011) Detection of Crossing White Matter Fibers with High-Order Tensors and Rank-k Decompositions. Information Processing in Medical Imaging: 538–549. Available: http://dx.doi.org/10.1007/978-3-642-22092-0_44.en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.doi10.1007/978-3-642-22092-0_44en
dc.identifier.urihttp://hdl.handle.net/10754/597954en
dc.description.abstractFundamental to high angular resolution diffusion imaging (HARDI), is the estimation of a positive-semidefinite orientation distribution function (ODF) and extracting the diffusion properties (e.g., fiber directions). In this work we show that these two goals can be achieved efficiently by using homogeneous polynomials to represent the ODF in the spherical deconvolution approach, as was proposed in the Cartesian Tensor-ODF (CT-ODF) formulation. Based on this formulation we first suggest an estimation method for positive-semidefinite ODF by solving a linear programming problem that does not require special parameterization of the ODF. We also propose a rank-k tensor decomposition, known as CP decomposition, to extract the fibers information from the estimated ODF. We show that this decomposition is superior to the fiber direction estimation via ODF maxima detection as it enables one to reach the full fiber separation resolution of the estimation technique. We assess the accuracy of this new framework by applying it to synthetic and experimentally obtained HARDI data. © 2011 Springer-Verlag.en
dc.description.sponsorshipThe authors would like to thank Tamara G. Kolda of San-dia National Labs, Livermore, California, for useful discussions and comments re-garding this work. This research wasfunded by the NIH grants: 5R01EB007688,5R01HL092055, and by the NIH/NCRR Center for Integrative Biomedical Com-puting, P41-RR12553-10, Award No. KUS-C1-016-04, made by King AbdullahUniversity of Science and Technology (KAUST), and DOE SciDAC VACET.en
dc.publisherSpringer Science + Business Mediaen
dc.titleDetection of Crossing White Matter Fibers with High-Order Tensors and Rank-k Decompositionsen
dc.typeBook Chapteren
dc.identifier.journalInformation Processing in Medical Imagingen
dc.contributor.institutionUniversity of Utah, Salt Lake City, United Statesen
kaust.grant.numberKUS-C1-016-04en
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