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      Manyetik parçacık görüntüleme için sinyal-gürültü oranını eniyileyen görüntü geriçatım tekniği

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      Author(s)
      Bozkurt, Ecem
      Sarıtaş, Emine Ülkü
      Date
      2017
      Source Title
      Journal of the Faculty of Engineering and Architecture of Gazi University
      Print ISSN
      1300-1884
      Publisher
      Gazi Universitesi Muhendislik-Mimarlik
      Volume
      32
      Issue
      3
      Pages
      999 - 1013
      Language
      Turkish
      Type
      Article
      Item Usage Stats
      193
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      151
      downloads
      Abstract
      Magnetic particle imaging (MPI) is a new biomedical imaging modality that images the spatial distribution of superpamagnetic iron oxide nanoparticles. In MPI, the amplitude of the excitation magnetic field that causes the time-varying magnetization response of the nanoparticles is restricted by the nerve stimulation safety limits. Hence, the region to be imaged is divided into small sections and scanned as overlapping partial fields-of-view. The nanoparticle signal at the excitation frequency is lost during the filtering process of the direct feedthrough signal induced on the receive coil due to the excitation field. To recover this loss, the overlapping partial fields-of-view are merged via utilizing the continuity and positivity of the desired image. In this work, an image reconstruction technique that merges the partial fields-of-view while optimizing the signal-to-noise ratio is proposed. Accordingly, each partial field-of-view must be weighted by the square of the position-dependent scanning speed. Via extensive simulations at various overlap percentages and signal-to-noise ratios, this work demonstrates that the proposed method overcomes the vertical line artifacts caused by the standard MPI reconstruction techniques and improves image quality.
      Keywords
      Angiography
      Image reconstruction
      Magnetic particle imaging
      Signal-to-noise ratio
      Superparamagnetic iron oxide nanoparticles
      Permalink
      http://hdl.handle.net/11693/37009
      Published Version (Please cite this version)
      http://dx.doi.org/10.17341/gazimmfd.337864
      Collections
      • Aysel Sabuncu Brain Research Center (BAM) 213
      • Department of Electrical and Electronics Engineering 3702
      • National Magnetic Resonance Research Center (UMRAM) 218
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