Parameter estimation in Bayesian reconstruction of SPECT images: An aid in nuclear medicine diagnosis

Antonio López*, Rafael Molina, Aggelos K. Katsaggelos, Antonio Rodriguez, José M. López, José M. Liamas

*Corresponding author for this work

Research output: Contribution to journalArticle

5 Scopus citations


Despite the adequacy of Bayesian methods to reconstruct nuclear medicine SPECT (single-photon emission computed tomography) images, they are rarely used in everyday medical practice. This is primarily because of their computational cost and the need to appropriately select the prior model hyperparameters. We propose a simple procedure for the estimation of these hyperparameters and the reconstruction of the original image and test the procedure on both synthetic and real SPECT images. The experimental results demonstrate that the proposed hyperparameter estimation method produces satisfactory reconstructions. Although we have used generalized Gaussian Markov random fields (GGMRF) as prior models, the proposed estimation method can be applied to any priors with convex potential and tractable partition function with respect to the scale hyperparameter.

Original languageEnglish (US)
Pages (from-to)21-27
Number of pages7
JournalInternational Journal of Imaging Systems and Technology
Issue number1
StatePublished - Aug 2 2004


  • Bayesian reconstruction
  • Hyperparameter estimation
  • Nuclear medicine
  • SPECT imaging

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Software
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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