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Proceedings Paper

A Multi Grid Maximum Likelihood Reconstruction Algorithm For Positron Emission Tomography
Author(s): Atam P. Dhawan; M. V. Ranganath; G. Ganti; N. Mullani; K. L. Gould
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Paper Abstract

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 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 non-uniform correction efficiency of each iteration making the algorithm very sensitive to the image-pattern. An efficient knowledge-based multi-grid reconstruction algorithm based on ML approach is presented to overcome these problems.

Paper Details

Date Published: 27 June 1988
PDF: 7 pages
Proc. SPIE 0914, Medical Imaging II, (27 June 1988); doi: 10.1117/12.968645
Show Author Affiliations
Atam P. Dhawan, University of Houston (United States)
M. V. Ranganath, University of Houston (United States)
G. Ganti, The University of Texas (United States)
N. Mullani, The University of Texas (United States)
K. L. Gould, The University of Texas (United States)

Published in SPIE Proceedings Vol. 0914:
Medical Imaging II
Samuel J. Dwyer; Roger H. Schneider; Samuel J. Dwyer; Roger H. Schneider; Roger H. Schneider; Samuel J. Dwyer, Editor(s)

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