• DocumentCode
    2616553
  • Title

    Theoretical and numerical study of MLEM and OSEM reconstruction algorithms for motion correction in emission tomography

  • Author

    Dey, Joyoni ; King, Michael A.

  • Author_Institution
    Division of Nuclear Medicine, Department of Radiology, University of Massachusetts Medical School, Worcester, USA
  • fYear
    2008
  • fDate
    19-25 Oct. 2008
  • Firstpage
    5179
  • Lastpage
    5186
  • Abstract
    Patient body-motion and respiratory-motion impacts the image quality of cardiac PET or SPECT perfusion images. Several algorithms exist in the literature to correct for motion within the iterative maximum-likelihood reconstruction framework. In this work, three algorithms are derived using Poisson statistics to correct for patient motion. The first one is a motion compensated MLEM algorithm (MC-MLEM). The next two algorithms called MGEM-1 and MGEM-2 (short for Motion Gated EM, 1 and 2) use the motion states as subsets, in two different ways. Experiments were performed with NCAT phantoms with exactly known motion as the source and attenuation distributions. The SIMIND Monte Carlo simulation software was used to create SPECT projection images of the NCAT phantoms. The projection images were then modified to have Poisson noise levels equivalent to that of clinical acquisition. We investigated application of these algorithms to correction of (1) a large body-motion of 2 cm in Superior-Inferior (SI) and Anterior-Posterior (AP) directions each and (2) respiratory motion of 2 cm in SI and 0.6 cm in AP. We obtained the bias with respect to the NCAT phantom activity for noiseless reconstructions as well as the bias-variance for noisy reconstructions. The MGEM-1 advanced along the biasvariance curve faster than the MC-MLEM with iterations. The MGEM-1 also lowered the noiseless bias (with respect to NCAT truth) faster with iterations, compared to the MC-MLEM algorithms, as expected with subset algorithms. For the body motion correction with two motion states, after the 9th iteration the bias was close to that of MC-MLEM at iteration 17, reducing the number of iterations by a factor of 1.89. For the respiratory motion correction with 9 motion states, based on the noiseless bias, the iteration reduction factor was approximately 7. For the MGEM-2, however, bias-plot or the bias-variance-plot saturates with iteration because of successive interpolation error.
  • Keywords
    Application software; Attenuation; Image quality; Image reconstruction; Imaging phantoms; Iterative algorithms; Noise level; Positron emission tomography; Reconstruction algorithms; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Nuclear Science Symposium Conference Record, 2008. NSS '08. IEEE
  • Conference_Location
    Dresden, Germany
  • ISSN
    1095-7863
  • Print_ISBN
    978-1-4244-2714-7
  • Electronic_ISBN
    1095-7863
  • Type

    conf

  • DOI
    10.1109/NSSMIC.2008.4774402
  • Filename
    4774402