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Deformable models and machine learning for large-scale cardiac MRI image analytics

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TitleInfo
Title
Deformable models and machine learning for large-scale cardiac MRI image analytics
Name (type = personal)
NamePart (type = family)
Yang
NamePart (type = given)
Dong
NamePart (type = date)
1987-
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Dong Yang
Role
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author
Name (type = personal)
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Metaxas
NamePart (type = given)
Dimitris N.
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Dimitris N. Metaxas
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Advisory Committee
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chair
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Michmizos
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Konstantinos
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Konstantinos Michmizos
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Advisory Committee
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internal member
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Zhang
NamePart (type = given)
Yongfeng
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Yongfeng Zhang
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Advisory Committee
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internal member
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Duncan
NamePart (type = given)
James
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James Duncan
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Advisory Committee
Role
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outside member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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school
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theses
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2019
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2019-05
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2019
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English
Abstract
The analysis of left ventricle (LV) wall motion is an important step for understanding cardiac functioning mechanisms, and clinical diagnosis of ventricular diseases. For example, ventricular dyssynchrony is one of the major causes for heart failure; treatment of dyssynchrony, e.g. Cardiac Resynchronization Therapy (CRT), can help some patients preventing failure. Conventional diagnosis methods, including electrocardiogram (ECG) and ultrasound imaging, provide only coarse characterization of dyssynchrony patterns, such as global function indices or qualitative assessment of motion patterns. To achieve a more comprehensive understanding of ventricular dyssynchrony, we propose a novel approach to study the regional patterns of left ventricle (LV) wall using cardiac magnetic resonance imaging (MRI). Firstly, we extract the myocardial contours from long- and short-axis cine MRI, and compensate for respiration offsets through rigid transformation to reconstruct the 3D shell of the heart wall. Then an unsupervised learning method using deep neural networks is adopted to compute the in-plane deformation field. Next, the 3D volumetric LV wall motion and deformation fields are recovered by using deformable models and spatial interpolation. Finally, in order to characterize the regional motion of the LV wall, a conventional 17-segment model is utilized for dividing the reconstructed 3D model, so that the local dyssynchrony patterns can be well-determined. Our proposed approach has a great potential to be applied in the analysis of large-scale MRI datasets of various cardiovascular diseases, and used to guide the administration of CRT. Moreover, we include other applications for further demonstration of our approaches.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Heart -- Magnetic resonance imaging
Subject (authority = ETD-LCSH)
Topic
Machine learning
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
Identifier
ETD_9853
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1 online resource (xvi, 97 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-x9v0-9704
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
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Yang
GivenName
Dong
Role
Copyright Holder
RightsEvent
Type
Permission or license
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2019-04-12 15:38:15
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Name
Dong Yang
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Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
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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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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