Bayesian PET image reconstruction incorporating anato-functional joint entropy

Jing Tang, Benjamin M.W. Tsui, Arman Rahmim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We developed a maximum a posterior (MAP) reconstruction method for PET image reconstruction incorporating MR image information, with the joint entropy between the PET and MR image features serving as the prior. A non-parametric method was used to estimate the joint probability density (JPD) of the PET and MR images. The sampling rate for Parzen window estimation of the JPD was studied for both simulated phantom and clinical FDG PET brain images. Using realistic simulated PET and MR brain phantoms, the quantitative performance of the proposed algorithm was investigated. In particular, variations in the weighting factor on the MAP prior as well as the variance in the Parzen window were examined. Incorporation of the anatomical information via this technique was seen to noticeably improve the noise vs. bias tradeoff in various regions of interest.

Original languageEnglish (US)
Title of host publication2008 5th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, Proceedings, ISBI
Pages1043-1046
Number of pages4
DOIs
StatePublished - Sep 10 2008
Event2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France
Duration: May 14 2008May 17 2008

Publication series

Name2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

Other

Other2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
CountryFrance
CityParis
Period5/14/085/17/08

Keywords

  • Anatomical priors
  • Bayesian image reconstruction
  • Joint entropy
  • Positron emission tomography

ASJC Scopus subject areas

  • Biomedical Engineering

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