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Proceedings Paper

Resampling scheme for improving maximum likelihood reconstructions of positron emission tomography images
Author(s): Kevin J. Coakley
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Paper Abstract

In a Maximum Likelihood approach, reconstructions of positron emissions tomography images are obtained with the iterative Expectation Maximization (EM) algorithm. After too many iterations, the reconstruction becomes too rough. In recent work, the EM algorithm was halted by a cross-validation procedure. However, at this stopping point, reconstructions still exhibited some undesirable roughness. Here, the variability of the reconstruction about its expected value is reduced by a Monte Carlo resampling scheme. For simulated data, reconstructions obtained by resampling were somewhat sharper than reconstructions obtained by a simpler linear filtering method. Real data from a FDG study is also studied. Near the boundaries, the Monte Carlo method yielded a sharper reconstruction.

Paper Details

Date Published: 11 May 1994
PDF: 11 pages
Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); doi: 10.1117/12.175061
Show Author Affiliations
Kevin J. Coakley, National Institute of Standards and Technology (United States)

Published in SPIE Proceedings Vol. 2167:
Medical Imaging 1994: Image Processing
Murray H. Loew, Editor(s)

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