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A quantitative data representation framework for structural and functional MR Imaging with application to prostate cancer detection
Descriptive
TitleInfo
Title
A quantitative data representation framework for structural and functional MR Imaging with application to prostate cancer detection
Name
(type = personal)
NamePart
(type = family)
Viswanath
NamePart
(type = given)
Satish Easwar
NamePart
(type = date)
1983-
DisplayForm
Satish Viswanath
Role
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(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Madabhushi
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(type = given)
Anant
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Anant Madabhushi
Affiliation
Advisory Committee
Role
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(authority = RULIB)
chair
Name
(type = personal)
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(type = family)
Shinbrot
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(type = given)
Troy
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Troy Shinbrot
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Ganesan
NamePart
(type = given)
Shridar
DisplayForm
Shridar Ganesan
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Feldman
NamePart
(type = given)
Michael
DisplayForm
Michael Feldman
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
outside member
Name
(type = personal)
NamePart
(type = family)
Bloch
NamePart
(type = given)
Nicolas
DisplayForm
Nicolas Bloch
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
outside member
Name
(type = corporate)
NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
(type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm
(authority = RULIB)
school
TypeOfResource
Text
Genre
(authority = marcgt)
theses
OriginInfo
DateCreated
(qualifier = exact)
2012
DateOther
(qualifier = exact);
(type = degree)
2012-05
CopyrightDate
(qualifier = exact)
2012
Place
PlaceTerm
(type = code)
xx
Language
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(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
Prostate cancer (CaP) is currently the second leading cause of cancer-related deaths in the United States among men, but there is a paucity of non-invasive image-based information for CaP detection and staging in vivo. Studies have shown the utility of multi-protocol magnetic resonance imaging (MRI) to improve CaP detection accuracy by using both T2-weighted (T2w), dynamic contrast enhanced (DCE), and diffusion weighted (DWI) MRI information. In this thesis, we present methods for quantitative representation of structural and functional imaging data with the objective of building automated classifiers to improve CaP detection accuracy in vivo. In vivo disease presence was quantified via extraction of textural signatures from T2w MRI. Evaluation of these signatures showed that CaP appearance within each of the two dominant prostate regions (central gland, peripheral zone) is significantly different. A classifier trained on zone-specific features also yielded a higher detection accuracy compared to a simpler, monolithic combination of all the texture features. While a number of automated classifiers are available, classifier choice must account for limitations in dataset size and annotation (such as with in vivo prostate MRI). A comprehensive evaluation of different classifier schemes was undertaken for the specific problem of automated CaP detection via T2w MRI on a zonewise basis. It was found that simple classifiers yielded significantly improved CaP detection accuracies compared to complex classifiers. Fundamental differences must be overcome when constructing a unified quantitative representation of structural (T2w) and functional (DCE, DWI) MRI. We present a novel technique, referred to as consensus embedding, which constructs a lower dimensional representation (embedding) from a high dimensional feature space such that information (class-based or otherwise) is optimally preserved. Consensus embedding is shown to result in an improved representation of the data compared to alternative DR-based strategies in a variety of experimental domains. A unified quantitative representation of T2w, DCE, and DWI prostate MRI was constructed via the consensus embedding framework. This yielded an integrated classifier which was more accurate for CaP detection in vivo as compared to using structural and functional information individually, or using a naive combination of such differing types of information.
Subject
(authority = RUETD)
Topic
Biomedical Engineering
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_3981
PhysicalDescription
Form
(authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xxiv, 134 p. : ill.
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Satish Easwar Viswanath
Subject
(authority = ETD-LCSH)
Topic
Cancer--Magnetic resonance imaging
Subject
(authority = ETD-LCSH)
Topic
Prostate--Magnetic resonance imaging
Subject
(authority = ETD-LCSH)
Topic
Prostate--Cancer--Diagnosis
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000065286
RelatedItem
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T3HQ3XVH
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Viswanath
GivenName
Satish
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2012-04-16 05:16:19
AssociatedEntity
Name
Satish Viswanath
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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