Joint-MAP reconstruction/segmentation for transmission tomography using mixture-models as priors

Ing Tsung Hsiao*, Anand Rangarajan, Gene Gindi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

A Bayesian method, including a pointwise prior comprising mixtures of gamma distributions, is applied to the problem of transmission tomography. A joint MAP (maximum a posteriori) procedure is proposed wherein the reconstruction itself, as well as all pointwise parameters, are calculated simultaneously. It uses an algorithm that successively refines the estimate of the mixture parameters and the reconstruction. The approach aims to solve the problem of low counts statistics in transmission tomography. Initial simulation results with anecdotal reconstruction show that the gamma mixture model likely outperforms the ML (maximum likelihood) method and FBP (filtered-backprojection) algorithm.

Original languageEnglish
Title of host publicationIEEE Nuclear Science Symposium and Medical Imaging Conference
PublisherIEEE
Pages1689-1693
Number of pages5
ISBN (Print)0780350227
StatePublished - 1999
Externally publishedYes
EventProceedings of the 1998 IEEE Nuclear Science Symposium Conference Record - Toronto, Que, Can
Duration: 08 11 199814 11 1998

Publication series

NameIEEE Nuclear Science Symposium and Medical Imaging Conference
Volume3

Conference

ConferenceProceedings of the 1998 IEEE Nuclear Science Symposium Conference Record
CityToronto, Que, Can
Period08/11/9814/11/98

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