Scatter correction in digital mammography based on image deconvolution

X-ray scatter is a major cause of nonlinearity in densitometry measurements using digital mammography. Previous scatter correction techniques have primarily used a single scatter point spread function to estimate x-ray scatter. In this study, a new algorithm to correct x-ray scatter based on image convolution was implemented using a spatially variant scatter point spread function which is energy and thickness dependent. The scatter kernel was characterized in terms of its scattering fraction (SF) and scatter radial extent (k) on uniform Lucite phantoms with thickness of 0.8-8.0 cm. The algorithm operates on a pixel-by-pixel basis by grouping pixels of similar thicknesses into a series of mask images that are individually deconvolved using Fourier image analysis with a distinct kernel for each image. The algorithm was evaluated with three Lucite step phantoms and one anthropomorphic breast phantom using a full-field digital mammography system at energies of 24, 28, 31 and 49 kVp. The true primary signal was measured with a multi-hole collimator. The effect on image quality was also evaluated. For all 16 studies, the average mean percentage error in estimating the true primary signal was found to be -2.13% and the average rms percentage error was 2.60%. The image quality was seen to improve at every energy up to 25% at 49 kVp. The results indicate that a technique based on a spatially variant scatter point spread function can accurately estimate x-ray scatter.