Wavelet domain ML reconstruction in positive emission tomography

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

Research output: Contribution to journalArticle

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 - 2000
Externally publishedYes

Fingerprint

Wavelet Analysis
Wavelet transforms
Maximum likelihood
Tomography
Parameter estimation
Brain

Keywords

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

ASJC Scopus subject areas

  • Bioengineering

Cite this

Wavelet domain ML reconstruction in positive emission tomography. / Kisilev, P.; Jacobson, M.; Zeevi, Y. Y.

In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, Vol. 1, 2000, p. 90-93.

Research output: Contribution to journalArticle

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