Bayesian 2-D deconvolution: A model for diffuse ultrasound scattering

Observed medical ultrasound images are degraded representations of the true acoustic tissue reflectance. The degradation is due to blur and speckle an d significantly reduces the diagnostic value of the images. To remove both blur and speckle, we have developed a new statistical model for diffuse sca ttering in 2-D ultrasound radio frequency images, incorporating both spatia l smoothness constraints and a physical model for diffuse scattering. The m odeling approach is Bayesian in nature, and we use Markov chain Monte Carlo methods to obtain the restorations. The results from restorations of some real and simulated radio frequency ultrasound images are presented and comp ared with results produced by Wiener filtering.