A Multigrid Expectation Maximization Reconstruction Algorithm for Positron Emission Tomography

M. V. Ranganath, Atam P. Dhawan, N. Mullani

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


The problem of reconstruction in positron emission tomography (PET) is basically estimating the number of photon pairs emitted from the source. Using the concept of the maximum likelihood (ML) algorithm, the problem of reconstruction is reduced to determining an estimate of the emitter density that maximizes the probability of observing the actual detector count data over all possible emitter density distributions. A solution using this type of expectation maximization (EM) algorithm with a fixed grid size is severely handicapped by the slow convergence rate, the large computation time, and the nonuniform correction efficiency of each iteration making the algorithm very sensitive to the image pattern. An efficient knowledge-based multigrid reconstruction algorithm based on the ML approach is presented to overcome these problems.

Original languageEnglish (US)
Pages (from-to)273-278
Number of pages6
JournalIEEE Transactions on Medical Imaging
Issue number4
StatePublished - Dec 1988
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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