Multicriterion Optimization for Projection Data in Computerized Tomography

It is an effective way for improving quality of reconstructed images that projection data are optimized in the field of Computerized Tomography (CT). For lowering the fuzziness on projection data caused by lots of factors such as noise and getting the unigue optimal reconstructed image from noisy projection data further more, in this paper, the authors develop a new model named Multicriterion Optimization Model (MOP) for optimizing projection data. The model is structured based on the theories of fuzzy mathematics and decision making, via the method of deducing projection fuzzy exponent function and square error fuzzy exponent function. The experiments for validating the model have been carried out on personal computer (PC). At first, we make simulation collecting in the images presented and add Gauss Noise into the projection data obtained by simulation collecting, Then, complete the image reconstruction from noisy projection data by the solution without optimization and another solution with multicriterion optimization. Finally, compare and analysis the different results about images reconstructed by two different solutions. The experiment results indicate that the MOP in this paper has better consistency with the theory and practice as well as obvious advantage of antinoise ability.

Multicriterion Optimization for Projection Data in Computerized Tomography

It is an effective way for improving quality of reconstructed images that projection data are optimized in the field of Computerized Tomography (CT). For lowering the fuzziness on projection data caused by lots of factors such as noise and getting the unigue optimal reconstructed image from noisy projection data further more, in this paper, the authors develop a new model named Multicriterion Optimization Model (MOP) for optimizing projection data. The model is structured based on the theories of fuzzy mathematics and decision making, via the method of deducing projection fuzzy exponent function and square error fuzzy exponent function. The experiments for validating the model have been carried out on personal computer (PC). At first, we make simulation collecting in the images presented and add Gauss Noise into the projection data obtained by simulation collecting, Then, complete the image reconstruction from noisy projection data by the solution without optimization and another solution with multicriterion optimization. Finally, compare and analysis the different results about images reconstructed by two different solutions. The experiment results indicate that the MOP in this paper has better consistency with the theory and practice as well as obvious advantage of antinoise ability.