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3-D morphometry and non-rigid registration for quantitative analysis and clinical assessment in radiology
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Title
3-D morphometry and non-rigid registration for quantitative analysis and clinical assessment in radiology
Name
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Niculescu
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Gabriela
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Gabriela Niculescu
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Foran
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David
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Advisory Committee
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David J Foran
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chair
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Metaxas
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Dimitris
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Advisory Committee
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Dimitris N Metaxas
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internal member
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Wilder
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Joseph
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Advisory Committee
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Joseph Wilder
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Nosher
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John
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Advisory Committee
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John L Nosher
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Rutgers University
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Graduate School - New Brunswick
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theses
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2008
DateOther
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2008-05
Language
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English
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electronic
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text/xml
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xii, 92 pages
Abstract
The capacity to reliably track, model and characterize morphometric changes in anatomic structures and tumors from 3-D images sequences is extremely valuable in staging disease progression and assessing response
to treatment.
We have designed, developed and evaluated two approaches to facilitate clinical assessment in diagnostic radiology. The first is a tool for
performing comparative morphologic analysis and the second is a registration strategy which can compensate for changes in shape that occur
in deformable organs when assessing response to treatment across consecutive imaging studies. The first prototype system was used to characterize the morphology of ventricles from MR brain scans of patients
who had been diagnosed with Bipolar Disorder or Asperger's Syndrome. Preliminary studies demonstrated that conventional volumetric measurements were insufficient for detecting and characterizing subtle changes in anatomic profiles. We have investigated the use of a double elliptic Fourier transform to discriminate among 3-D changes of anatomic structures. It was shown that characterization using low frequency elliptic Fourier descriptors provided an accurate representation of the anatomical structures while allowing for reliable group separation. The shape-based 3-D object representation of brain structures developed in
this project may provide insight regarding the underlying mechanisms leading to the onset and progression of these disorders.
As an extension of these studies, a deformable registration technique was evaluated for tracking tumor response to radiofrequency ablation of
patients with liver malignancies. The method exploits the combined power of global and local alignment of pre- and post-treatment CT images. The distinguishing characteristics of the system is that it can infer volumetric deformation based upon surface displacements using a linearly elastic finite element model (FEM). Using both 2-D synthetic phantoms and
3-D beef liver data we performed the simulation of gold standard registration by measuring the accuracy of non-rigid deformation. The sub-voxel mean displacement error of deformation demonstrates that the technique provides valuable information for surgical interventions. This approach is general methodology for tracking deformable organs using non-rigid registration with respect to FEM simulations. It provides a basis for monitoring tissue response and therapy planning for a range of medical applications in the brain, breast or heart.
to treatment.
We have designed, developed and evaluated two approaches to facilitate clinical assessment in diagnostic radiology. The first is a tool for
performing comparative morphologic analysis and the second is a registration strategy which can compensate for changes in shape that occur
in deformable organs when assessing response to treatment across consecutive imaging studies. The first prototype system was used to characterize the morphology of ventricles from MR brain scans of patients
who had been diagnosed with Bipolar Disorder or Asperger's Syndrome. Preliminary studies demonstrated that conventional volumetric measurements were insufficient for detecting and characterizing subtle changes in anatomic profiles. We have investigated the use of a double elliptic Fourier transform to discriminate among 3-D changes of anatomic structures. It was shown that characterization using low frequency elliptic Fourier descriptors provided an accurate representation of the anatomical structures while allowing for reliable group separation. The shape-based 3-D object representation of brain structures developed in
this project may provide insight regarding the underlying mechanisms leading to the onset and progression of these disorders.
As an extension of these studies, a deformable registration technique was evaluated for tracking tumor response to radiofrequency ablation of
patients with liver malignancies. The method exploits the combined power of global and local alignment of pre- and post-treatment CT images. The distinguishing characteristics of the system is that it can infer volumetric deformation based upon surface displacements using a linearly elastic finite element model (FEM). Using both 2-D synthetic phantoms and
3-D beef liver data we performed the simulation of gold standard registration by measuring the accuracy of non-rigid deformation. The sub-voxel mean displacement error of deformation demonstrates that the technique provides valuable information for surgical interventions. This approach is general methodology for tracking deformable organs using non-rigid registration with respect to FEM simulations. It provides a basis for monitoring tissue response and therapy planning for a range of medical applications in the brain, breast or heart.
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references (p. 84-91).
Subject
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Topic
Biomedical Engineering
Subject
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Topic
Radiology, Medical
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17364
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ETD_871
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NjNbRU
Identifier
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doi:10.7282/T39S1RC7
Genre
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ETD doctoral
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The author owns the copyright to this work.
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Copyright protected
Availability
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Open
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Name
Gabriela Niculescu
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Affiliation
Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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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.
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