Minimum cross-entropy reconstruction of PET images using prior anatomical information

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Abstract
An algorithm is presented for the reconstruction of PET images using prior anatomical information derived from MR images of the same subject. The cross-entropy or Kullback-Leiber distance is a measure of dissimilarity between two images. We propose to reconstruct PET images by minimizing a weighted sum of two cross-entropy terms. The first is the cross-entropy between the measured emission data and the forward projection of the current estimate of the PET image. Minimizing this term alone is equivalent to the ML-EM reconstruction. The second term is the cross-entropy between the current estimate of the PET image and a prior image model which incorporates anatomical information derived from registered MR images. A weighting parameter determines the relative emphasis given to the emission data and the prior model in the reconstruction. Details of this algorithm are presented as well as test reconstructions for real and simulated data. The performance of the algorithm was evaluated with respect to errors in prior anatomical information. The algorithm provided significant improvement in the quality of reconstructed images as compared with the ML-EM reconstruction technique. The reconstructed images had higher resolution as compared with the images obtained from MAP-like reconstructions which do not utilize anatomical information. The algorithm displayed robustness with respect to errors in prior anatomical information.