STATISTICAL-MODELS OF PARTIAL VOLUME EFFECT

Statistical models of partial volume effect for systems with various t ypes of noise or pixel value distributions are developed and probabili ty density functions are derived. The models assume either Gaussian sy stem sampling noise or intrinsic material variances with Gaussian or P oisson statistics. In particular, a material can be viewed as having a distinct value that has been corrupted by additive noise either befor e or after partial volume mixing, or the material could have nondistin ct values with a Poisson distribution as might be the case in nuclear medicine images. General forms of the probability density functions ar e presented for the N material cases and particular forms for two- and three-material cases are derived. These models are incorporated into finite mixture densities in order to more accurately model the distrib ution of image pixel values, Examples are presented using simulated hi stograms to demonstrate the efficacy of the models for quantification. Modeling of partial volume effect is shown to be useful when one of t he materials is present in images mainly as a pixel component.