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Sparsity-based methods for cardiac magnetic resonance image reconstruction and analysis
Descriptive
TitleInfo
Title
Sparsity-based methods for cardiac magnetic resonance image reconstruction and analysis
Name
(type = personal)
NamePart
(type = family)
Yu
NamePart
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Yang
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Yang Yu
Role
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author
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Metaxas
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Dimitris
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Dimitris Metaxas
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Advisory Committee
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chair
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Elgammal
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Ahmed
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Ahmed Elgammal
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Advisory Committee
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internal member
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(type = personal)
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(type = family)
Bekris
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(type = given)
Kostas
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Kostas Bekris
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Advisory Committee
Role
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(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Huang
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(type = given)
Xiaolei
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Xiaolei Huang
Affiliation
Advisory Committee
Role
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(authority = RULIB)
outside member
Name
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Rutgers University
Role
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(authority = RULIB)
degree grantor
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NamePart
Graduate School - New Brunswick
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school
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Text
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theses
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2015
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2015-10
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2015
Place
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xx
Language
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eng
Abstract
(type = abstract)
In signal processing, sparseness means that there are only small amounts of non-zero elements. This property has been widely observed in various types of signals. However, the data sparseness is hard to be regularized due to its non-convex nature. The recent development of the compressed sensing technique builds a theoretical connection between the sparse constraint and its convex relaxation. This discovery motivates us to explore different types of sparse properties for the generation and analysis of the cardiac magnetic resonance images (MRIs). In this work, our proposed a series of sparse optimization algorithms have been applied to cardiac image reconstruction, segmentation and motion tracking problems for fast and robust analyzing the cardiac data. The cardiac imaging is a challenging problem to MRI due to its fast motion. We proposed a novel calibration-less algorithm to accelerate the generation of dynamic MR images with both compressed sensing and parallel imaging. In addition to the temporal signal, which usually provides more data redundancy than spatial signals, the strong correlations among signals from different coils are utilized to form joint sparse constraints. A general optimization framework is presented to solve the problem under different types of temporal sparse constraints efficiently. We then apply the sparse constraint to the cardiac muscle motion tracking. The 3D deformable heart model is built by simulating its motion in a cardiac cycle based on tagged MRI. The tagged MR data is widely used to reveal the internal myocardial motion. However, the automated tagging line detection results are very noisy due to the poor image quality. To alleviate this issue, we introduce a new family of sparse deformable models based on the sparseness of the detection noise. Our new models track the heart motion robustly, and the resulting strains are consistent with those calculated from manual labels.
Subject
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Topic
Computer Science
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Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD_6691
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xiv, 103 p. : ill.)
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Subject
(authority = ETD-LCSH)
Topic
Magnetic resonance imaging
Subject
(authority = ETD-LCSH)
Topic
Heart--Imaging
Note
(type = statement of responsibility)
by Yang Yu
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
Location
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NjNbRU
Identifier
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doi:10.7282/T3W66NSP
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Yu
GivenName
Yang
Role
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Type
Permission or license
DateTime
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2015-09-01 17:52:46
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Name
Yang Yu
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Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
DateTime
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(qualifier = exact);
(point = start)
2015-10-31
DateTime
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2017-10-30
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 30th, 2017.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical
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ContentModel
ETD
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windows xp
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