Multicriterion maximum entropy image reconstruction from projections

Vector entropy imaging theory with application to computerized tomography

Medical imaging theory for x-ray CT and PET is based on image reconstruction from projections. In this paper a novel vector entropy imaging theory under the framework of multiple criteria decision making is presented. We also study the most frequently used image reconstruction methods, namely, least square, maximum entropy, and filtered back-projection methods under the framework, of the single performance criterion optimization. Finally, we introduce some of the results obtained by various reconstruction algorithms using computer-generated noisy projection data from the Hoffman phantom and real CT scanner data. Comparison of the reconstructed images indicates that the vector entropy method gives the best in error (difference between the original phantom data and reconstruction), smoothness (suppression of noise), grey value resolution and is free of ghost images.

Multicriterion cross-entropy minimization approach to positron emission tomographic imaging

A multicriterion cross-entropy minimization approach to positron emission tomographic (PET) imaging is described. An unexplored multicriterion cross-entropy optimization algorithm based on weighted-sum scalarization is used to solve this problem. The efficacy of the algorithm is compared with that of the single-criterion optimization algorithm and the convolution backprojection method for image reconstruction from computer-generated projection data and Siemens PET scanner data. The algorithms described have been implemented on a PIII/686 microcomputer.

Image restoration and reconstruction with a Bayesian approach

We have extended Johnson's Bayesian method for image restoration and reconstruction by introducing diagonal line sites, using symmetric neighborhood configurations, and employing an additional hyperparameter for estimation of line sites. A general formulation for arbitrary neighborhood configurations was derived. The major part of this paper deals with the conduct of computer simulations intended to examine the effect of the hyperparameters, the diagonal line sites, and the size of the neighborhood configuration on the performance of the proposed Bayesian method. We show that, for optimal performance, distinct hyperparameters should be used for the intensity sites and line sites. The results also suggest that a large neighborhood configuration should be used. By comparing the near-optimal restored images, we demonstrated that the use of diagonal line sites, along with the symmetric configurations thus made possible, can effectively remove the blocky edge artifacts and produce images of better quality. When the method was applied to positron emission tomography (PET) image reconstruction, our results showed that the quality of the reconstructed images was improved for both computer-simulated and real patient PET data.