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Bayesian Reconstruction for Transmission Tomography with Scale Hyperparameter Estimation

  • Antonio López
  • Rafael Molina
  • Aggelos K. Katsaggelos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3523)

Abstract

In this work we propose a new method to estimate the scale hyperparameter for transmission tomography in Nuclear Medicine image reconstruction problems. Within the Bayesian paradigm, Evidence Analysis and circulant preconditioners are used to obtain the scale hyperparameter. For the prior distribution, we use Generalized Gaussian Markov Random Fields (GGMRF), a nonquadratic function that preserves the edges in the reconstructed image. The experimental results indicate that the proposed method produces satisfactory reconstructions.

Keywords

Partition Function Evidence Analysis Bayesian Paradigm Attenuation Correction Factor Transmission Tomography 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Antonio López
    • 1
  • Rafael Molina
    • 2
  • Aggelos K. Katsaggelos
    • 3
  1. 1.Universidad de Granada, Departamento de Lenguajes y Sistemas InformáticosGranadaSpain
  2. 2.Departamento de Ciencias de la Computación e I.AUniversidad de GranadaGranadaSpain
  3. 3.Department of Electrical and Computer EngineeringNorthwestern UniversityEvaston

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