A Bayesian Reconstruction Algorithm for Emission Tomography using a Markov Random Field Prior

Tom Hebert; Richard Leahy

doi:10.1117/12.953287

25 May 1989 A Bayesian Reconstruction Algorithm for Emission Tomography using a Markov Random Field Prior

Tom Hebert, Richard Leahy

Proceedings Volume 1092, Medical Imaging III: Image Processing; (1989) https://doi.org/10.1117/12.953287
Event: Medical Imaging III, 1989, Newport Beach, CA, United States

Abstract

A Bayesian generalized expectation - maximization (GEM) algorithm using a locally correlated Markov random field prior in the form of a Gibbs function is developed for emission tomography. A close-form coordinate gradient ascent M-step which updates the image pixels sequentially is derived. The resulting GEM Bayesian algorithm is applied to estimating the 3-D image parameters in the Poisson model of emission sources based upon simulation of a parallel collimated gamma camera.

Citation Download Citation

Tom Hebert and Richard Leahy "A Bayesian Reconstruction Algorithm for Emission Tomography using a Markov Random Field Prior", Proc. SPIE 1092, Medical Imaging III: Image Processing, (25 May 1989); https://doi.org/10.1117/12.953287

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available