KUKU

KU ScholarWorks

  • myKU
  • Email
  • Enroll & Pay
  • KU Directory
    • Login
    View Item 
    •   KU ScholarWorks
    • Dissertations and Theses
    • Dissertations
    • View Item
    •   KU ScholarWorks
    • Dissertations and Theses
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Motion Estimation in Static Magnetic Resonance Elastography

    Thumbnail
    View/Open
    Popel_ku_0099D_10611_DATA_1.pdf (3.733Mb)
    Issue Date
    2009-12-09
    Author
    Popel, Elena
    Publisher
    University of Kansas
    Format
    110 pages
    Type
    Dissertation
    Degree Level
    Ph.D.
    Discipline
    Electrical Engineering & Computer Science
    Rights
    This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
    Metadata
    Show full item record
    Abstract
    Elastography is the imaging of the biomechanical properties of a tissue to detect and diagnose abnormal pathologies in a variety of disease conditions. Static Magnetic Resonance Elastography (MRE) is a modality of elastography that uses Magnetic Resonance Imaging (MRI) principles for data acquisition from a biological sample under external loading. An estimation of the mechanical deformation of the loaded sample from its Magnetic Resonance (MR) images constitutes a key component of the static MRE. Efforts in this area of research have mainly been focused on developing data acquisition protocols and motion estimation algorithms for producing high quality elastography images. So far, however, progress made in static MRE remains limited in both clinical and experimental fields. This dissertation work performed a comprehensive investigation of the data acquisition, pre-processing, and motion analysis stages of the static MRE modality. First, a mechanical device was introduced to reliably apply repetitive external compression to the sample. The design of this device and how it was interfaced with the scanner for gated data acquisition are described in detail. Next, MRI basics are summarized, and the use of tagged MRI sequence as the data acquisition protocol is justified. Optimal parameters that led to the best quality tagged MRI data were determined by taking the repetitiveness of the compression and the use of tag lines into consideration. Lastly, two reliable motion estimation algorithms were implemented and successfully tested on a variety of synthetic and real MRE data. After adjusting the parameters of the techniques using the prior knowledge of the features of the tagged MR images, both Iterative and One-step Optical Flow (OF) algorithms consistently produced acceptable results. It was found, that while applied to the real data, the Iterative OF algorithm slightly outperforms the One-step OF algorithm. The results of the testing are provided and discussed. This research is interdisciplinary and embraces concepts from the fields of Physics, Image Processing, Computer Vision, Algorithmics, Electrical Engineering, and Biomedical sciences. Future extensions of the research include a variety of studies on phantoms with an inclusion, small oncology animal models, and possibly followed by clinical human research that would contribute to improving the reliability, accuracy, and speed of tumor detection. Other possible applications may involve processing of different types of MRI data, such as cardiac tagged gated MRI.
    URI
    http://hdl.handle.net/1808/5667
    Collections
    • Engineering Dissertations and Theses [1055]
    • Dissertations [4473]

    Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.


    We want to hear from you! Please share your stories about how Open Access to this item benefits YOU.


    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

    Browse

    All of KU ScholarWorksCommunities & CollectionsThis Collection

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

    The University of Kansas
      Contact KU ScholarWorks
    Lawrence, KS | Maps
     
    • Academics
    • Admission
    • Alumni
    • Athletics
    • Campuses
    • Giving
    • Jobs

    The University of Kansas prohibits discrimination on the basis of race, color, ethnicity, religion, sex, national origin, age, ancestry, disability, status as a veteran, sexual orientation, marital status, parental status, gender identity, gender expression and genetic information in the University’s programs and activities. The following person has been designated to handle inquiries regarding the non-discrimination policies: Director of the Office of Institutional Opportunity and Access, IOA@ku.edu, 1246 W. Campus Road, Room 153A, Lawrence, KS, 66045, (785)864-6414, 711 TTY.

     Contact KU
    Lawrence, KS | Maps