3D simulation of pet brain images using segmented MRI data and positron tomograph characteristics

https://doi.org/10.1016/0895-6111(93)90030-QGet rights and content

Abstract

A 3D simulation procedure has been developed to generate simulated PET brain images from MRI data. MRI slices were segmented into gray matter, white matter, CSF structures, and assigned with radionuclide distributions. Projections through these regions were generated according to physical characteristics of a positron tomograph, including 31) sampling and resolution, attenuation, scatter, randoms, and counting statistics. The projection data were then reconstructed by filtered backprojection. The procedure was validated with a cold spot phantom. Simulated PET images for cerebral blood flow or metabolism are presented, along with a brief discussion of some applications and limitations.

References (12)

  • C.J. Thompson et al.

    PETSIM: Monte Carlo simulation of all sensitivity and resolution parameters of cylindrical positron imaging; systems

    Phys. Med. Biol.

    (1992)
  • D.K. Mahoney et al.

    A realistic computersimulated brain phantom far evaluation of PET characteristics

    IEEE Trans. Med. Imag.

    (1987)
  • G.D. Hutchins

    Simulation of signal recovery in PET studies of cerebral physiology and biochemistry

  • M. Kamber et al.

    Model-based 3D segmentation of multiple sclerosis lesions in dual-echo MRI data

  • Y. Picard et al.

    Improving the precision and accuracy of Monte Carlo simulation in PET

    IEEE Trans. Nucl. Sci.

    (1992)
  • A.C. Evans et al.

    Performance evaluation of the PC-2048: a new 15-slice encodedcrystal PET scanner for neurological studies

    IEEE Trans. Med. Imag.

    (1991)
There are more references available in the full text version of this article.

Cited by (21)

  • Evaluating similarity measures for brain image registration

    2013, Journal of Visual Communication and Image Representation
    Citation Excerpt :

    Then we assigned 3D distributions of the tracer concentration and tissue attenuation coefficient throughout the segmented brain images, projected data through these distributions according to the PET acquisition geometry, and incorporated physical effects associated with data acquisition (i.e. photon attenuation, scatter, and random and statistical noise). Finally, we reconstructed a set of projections using the filtered back projection algorithm [35]. Every experiment in the next section was performed on all five modalities of the images stated in this section, and the average outcome is reported as the results.

  • Modeling and simulation of 4D PET-CT and PET-MR images

    2013, PET Clinics
    Citation Excerpt :

    An attempt to address the need for computational speed is the development of fast analytic simulation packages. A rigorous approach has first been implemented by Ma and colleagues,15,69 who has shown realistic simulations of brain PET data derived from MR imaging measurements. In these investigations analytic approaches to simulate different physical effects have been developed.

View all citing articles on Scopus
View full text