BAYESIAN RECONSTRUCTION BASED ON FLEXIBLE PRIOR MODELS

A new approach to Bayesian reconstruction is proposed that endows the prior probability distribution with an inherent geometrical flexibilit y, which is achieved through a transformation of the coordinate system of the prior distribution or model into that of the reconstruction. W ith this warping, prior morphological information regarding the object that is being reconstructed may be adapted to various degrees to matc h the available measurements. The extent of warping is readily control led through the prior probability distributions that are specified for the warp parameters. The complete reconstruction consists of a warped version of the prior model plus an estimated deviation from the warpe d model. Examples of tomographic reconstructions demonstrate the power of this approach.