Properties of minimum cross-entropy reconstruction of emission tomography with a morphologically based prior

The authors have studied the properties of a minimum cross-entropy (MXE) algorithm for emission tomography reconstruction with a morphological prior. MXE is formulated with two terms: a maximum likelihood expectation maximization term and a penalty term for regularization within morphologically defined boundaries. The relative emphasis put on the two competing terms is controlled by the regularization constant /spl beta/. Edge resolution and noise were compared for reconstructions with and without corresponding prior. The prior leads to significant edge enhancement with edge resolution converging to a theoretical limit independent of /spl beta/. Normalised standard deviation (NSD) and resolution both illustrate that regularization within boundaries behaves predictably with more smoothing for larger /spl beta/. Application of ordered subsets (OS) was also investigated. For OS, edge enhancement is fully preserved but NSD increases for low subset size. Results demonstrate that OS is applicable to MXE provided subset size is greater than 4. OS-MXE has appealing properties for regularized reconstruction.