Mondal, Partha Pratim and Rajan, K (2003) Conditional Entropy Maximization for PET. In: IEEE International Conference on Systems, Man and Cybernetics, 2003, 5-8 October, USA, Vol.1, 697 -702.
Maximum Likelihood (ML) estimation is extensively used for estimating emission densities from clumped and incomplete measurement data in Positron Emission Tomography (PET) modality. Reconstruction produced by ML-algorithm has been found noisy because it does not make use of available prior knowledge. Bayesian estimation provides such a platform for the inclusion of prior knowledge in the reconstruction procedure. But modeling a prior distribution is a cumbersome task and needs a lot of insight. In this work, we have proposed a conditional entropy maximization algorithm for PET modality, which is a generalization of maximum likelihood algorithm. We have imposed self-normalization condition for the determination of prior distribution. It is found that as prior distribution tends to uniform distribution, the conditional entropy maximization algorithm reduces to maximum likelihood algorithm. Simulated experimental results have shown that images reconstructed using maximum entropy algorithm are qualitatively better than those generated by ML-algorithm.
|Item Type:||Conference Paper|
|Additional Information:||Copyright 1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Department/Centre:||Division of Physical & Mathematical Sciences > Physics|
|Date Deposited:||10 Jan 2006|
|Last Modified:||19 Sep 2010 04:22|
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