IMPROVED BAYESIAN IMAGE ESTIMATION FOR DIGITAL CHEST RADIOGRAPHY

Purpose: Previously, we have shown that Spatially Varying Bayesian Ima ge Estimation (SVBIE) can be used to reduce scatter and improve contra st-to-noise ratios (CNR) in digital chest radiographs with no degradat ion of image resolution. This previous algorithm used a model for scat ter compensation that was derived for emission tomography. Here, we de velop and evaluate a new iterative SVBIE technique that incorporates a scatter model derived for projection radiography. Materials and Metho ds: Portable digital radiographs of an anthropomorphic chest phantom w ere obtained along with quantitative scatter measurements using a cali brated photostimulable phosphor system. The new iterative SVBIE techni que was applied to the phantom image to reduce scatter. Scatter fracti on reduction, CNR improvement, and resolution degradation were evaluat ed. Results: Residual scatter fractions were reduced to less than 2% i n the lungs and 30% in the mediastinum at 14 iterations. CNR was impro ved by approximately 50% in the lung region and 187% in the mediastinu m. Resolution was not degraded. Conclusions: The new SVBIE technique c an reduce scatter to levels far below those provided by an antiscatter grid and can increase CNR without loss of resolution. The new techniq ue outperforms the previous Bayesian techniques. (C) 1997 American Ass ociation of Physicists in Medicine.