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Bayesian PET Image Reconstruction Incorporating Anato-functional Joint Entropy.pdf

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Summary

A Bayesian PET image reconstruction method incorporating anato-functional joint entropy is proposed. The method uses a maximum a posteriori (MAP) reconstruction algorithm with the joint entropy between PET and MR image features as the prior. A non-parametric method is used to estimate the joint probability density of the PET and MR images. The algorithm is tested using simulated brain phantoms and clinical FDG PET brain images, and its performance is evaluated using noise vs. bias tradeoff curves for different regions of interest. The results show that incorporating anatomical information via this technique improves the noise vs. bias tradeoff in various regions of interest.

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A Bayesian PET image reconstruction method incorporating anato-functional joint entropy is proposed.

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  • File Format: PDF
  • File Size: 162 KB
  • Pages: 4
  • Language: EN
  • Total Downloads: 58
  • Last Updated: 1 week ago

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