Wavelet domain ML reconstruction in positive emission tomography

P. Kisilev, M. Jacobson, Y. Y. Zeevi

Research output: Contribution to journalConference article

Abstract

A classic technique for reconstruction of Positive Emission Tomography (PET) images from measured projections is based on the maximum likelihood (ML) parameter estimation along with the Expectation Maximization (EM) algorithm. We incorporate the Wavelet transform (WT) into the ML framework, and obtain a new iterative algorithm that incorporates local and multiresolution properties of the WT within the structure of the EM. Using the WT allows one to embed regularization procedures (filtering) into the iterative process, by imposing a new set of parameters on a subset of wavelet coefficients with a desired resolution. Properties of the proposed algorithm are demonstrated on reconstructions of a synthetic brain phantom.

Original languageEnglish (US)
Pages (from-to)90-93
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume1
StatePublished - Dec 1 2000
Event22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, United States
Duration: Jul 23 2000Jul 28 2000

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Keywords

  • Expectation maximization
  • Medical imaging
  • Positive emission tomography
  • Wavelets

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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