• DocumentCode
    3535087
  • Title

    Performance evaluation of iterative image reconstruction algorithms for non-sparse object reconstruction

  • Author

    Singh, Santosh

  • Author_Institution
    Siemens Corp. Technol. - India, Bangalore, India
  • fYear
    2010
  • fDate
    Oct. 30 2010-Nov. 6 2010
  • Firstpage
    3245
  • Lastpage
    3247
  • Abstract
    Partially regularized technique is an extension based on interval constraint of minimum norm solution. Such techniques have shown good results on problems like Missing Data Recovery (MDR). The proposed use of partially regularized technique for the computer tomography (CT) image reconstruction is to investigate if the MDR concept can be used for few view projection data acquisition scenario. The motivation for such an implementation is to establish a concept of MDR in sparse CT image reconstruction. In the present initial conceptual work, the sparse CT image reconstruction means highly under-sampled data.
  • Keywords
    computerised tomography; image reconstruction; iterative methods; medical image processing; computer tomography image reconstruction; highly undersampled data; iterative image reconstruction algorithms; minimum norm solution; missing data recovery; nonsparse object reconstruction; partially regularized technique; performance evaluation; projection data acquisition scenario; sparse CT image reconstruction; Computed tomography; Data acquisition; Equations; Image reconstruction; Mathematical model; Shape; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Nuclear Science Symposium Conference Record (NSS/MIC), 2010 IEEE
  • Conference_Location
    Knoxville, TN
  • ISSN
    1095-7863
  • Print_ISBN
    978-1-4244-9106-3
  • Type

    conf

  • DOI
    10.1109/NSSMIC.2010.5874404
  • Filename
    5874404