rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Performance analysis between two sparsity constrained mri methods
    sub title: highly constrained backprojection(hypr) and compressed sensing(cs) for dynamic imaging
  name:
    name part: Arzouni, Nibal.
    role:
      role term: creator
  name:
    name part: Ji, Jim X.
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: One of the most important challenges in dynamic magnetic resonance imaging (MRI) is to achieve high spatial and temporal resolution when it is limited by system performance. It is desirable to acquire data fast enough to capture the dynamics in the image time series without losing high spatial resolution and signal to noise ratio. Many techniques have been introduced in the recent decades to achieve this goal. Newly developed algorithms like Highly Constrained Backprojection (HYPR) and Compressed Sensing (CS) reconstruct images from highly undersampled data using constraints. Using these algorithms, it is possible to achieve high temporal resolution in the dynamic image time series with high spatial resolution and signal to noise ratio (SNR). In this thesis we have analyzed the performance of HYPR to CS algorithm. In assessing the reconstructed image quality, we considered computation time, spatial resolution, noise amplification factors, and artifact power (AP) using the same number of views in both algorithms, and that number is below the Nyquist requirement. In the simulations performed, CS always provides higher spatial resolution than HYPR, but it is limited by computation time in image reconstruction and SNR when compared to HYPR. HYPR performs better than CS in terms of SNR and computation time when the images are sparse enough. However, HYPR suffers from streaking artifacts when it comes to less sparse image data.
  note: by Nibal Arzouni.
  note: "Major Subject: Electrical Engineering"
  note: Electronic resource.
  note: Master of Science Texas A&M University 2010 http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8449
  note: Includes bibliographical references.
  note: Text (Thesis)
  subject:
    topic: Major Electrical Engineering
  subject:
    topic: sparsity
  subject:
    topic: image reconstruction
  subject:
    topic: compressed sensing
  subject:
    topic: undersampled data
  subject:
    topic: Highly constrained backprojection
  subject:
    topic: Dynamic MRI
  subject:
    topic: projection imaging
  identifier: http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8449
  record info:
    record content source: TXA
    record creation date: 121127
    record change date: 20150922151219.0
    record identifier: in00002783520