Spatially varying scatter compensation for digital chest radiography

Previously, we have shown the effectiveness of using Bayesian image estimation (BIE) to reduce scatter and increase the contrast to noise ratio (CNR) in digital chest radiography without degradation of resolution. Here, we investigate the incorporation of a spatially varying scatter model. Previously, BIE used a simple model for scatter, where scatter was modeled as a spatially invariant radial exponential with a single full-width at half-maximum and magnitude. This invariance resulted in some overcompensation and some undercompensation. A new spatially varying scatter model, where each pixel can have a different scatter kernel magnitude, was incorporated into BIE and used to reduce scatter in quantitative chest radiographs. Scatter fractions were reduced to less than 3% in the lung and mediastinum at 8 iterations. The original BIE technique only reduced scatter fractions to less the 2% in the lung and 38% in the mediastinum. CNR was improved by approximately 60% in the lung region and 200% in the mediastinum. No degradation of resolution was measured. Visual inspection showed improvement of image quality. Incorporation of a spatially varying scatter model into BIE reduces scatter to levels which far exceed those provided by an anti-scatter grid and can increase CNR without loss of resolution.