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Incorporation of anatomical MR data for improved functional imaging with PET

  • R Leahy
  • X Yan
2. Incorporation Of Priors In Tomographic Reconstraction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 511)

Abstract

A statistical approach to PET image reconstruction offers several potential advantages over the filtered backprojection method currently employed in most clinical PET systems: (1) the true data formation process may be modeled accurately to include the Poisson nature of the observation process and factors such as attenuation, scatter, detector efficiency and randoms; and (2) an a priori statistical model for the image may be employed to model the generally smooth nature of the desired spatial distribution and to include information such as the presence of anatomical boundaries, and hence potential discontinuities, in the image. In this paper we develop a Bayesian algorithm for PET image reconstruction in which a magnetic resonance image is used to provide information about the location of potential discontinuities in the PET image. This is achieved through the use of a Markov random field model for the image which incorporates a “line process” to model the presence of discontinuities. In the case where no a priori edge information is available, this line process may be estimated directly from the data. When edges are available from MR images, this information is introduced as a set of known a priori line sites in the image. It is demonstrated through computer simulation, that the use of a line process in the reconstruction process has the potential for significant improvements in reconstructed image quality, particularly when prior MR edge information is available.

Keywords

Positron Emission Tomography Markov Random Field MAP Reconstruction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • R Leahy
    • 1
  • X Yan
    • 1
  1. 1.Signal and Image Processing Institute, Department of Electrical Engineering-SystemsUniversity of Southern CaliforniaLos Angeles

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