Bayesian multiscale tomographic reconstruction

R. D. Nowak, E. Kolaczyk, D. Lalush, B. Tsui

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


This paper describes a new Bayesian modeling and analysis method for emission computed tomography based on a novel multiscale framework. The class of multiscale priors has the interesting feature that the «non-informative» member yields the traditional maximum likelihood solution; other choices are made to reflect prior belief as to the smoothness of the unknown intensity. Remarkably, this Bayesian multiscale framework admits a novel maximum a posteriori (MAP) reconstruction procedure using an expectation-maximization (EM) algorithm, in which the EM update equations have simple, closed-form expressions. The potential of this new framework is assessed using the Zubal brain phantom and simulated SPECT studies.

Original languageEnglish (US)
Title of host publicationDesign and Implementation of Signal Processing SystemNeural Networks for Signal Processing Signal Processing EducationOther Emerging Applications of Signal ProcessingSpecial Sessions
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)0780362934
StatePublished - 2000
Externally publishedYes
Event25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 - Istanbul, Turkey
Duration: Jun 5 2000Jun 9 2000

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149


Conference25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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