Scatter estimation for a digital radiographic system using convolution filtering

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Abstract
The use of a convolution‐filtering method to estimate the scatter distribution in images acquired with a digital subtraction angiography (DSA) imaging system has been studied. Investigation of more than 175 convolution kernels applied to images of anthropomorphic head, chest, and pelvic phantoms using 15‐, 25‐, and 36‐cm fields of view (digitized onto a 512×512 pixel image matrix) showed that two‐dimensional exponential kernels with a full width at half maximum (FWHM) of 50–150 pixels best reproduced the scatter fields within these images with a root‐mean‐square percentage error from 4% to 8%. A two‐dimensional exponential kernel with a FWHM of 75 pixels in each dimension applied to ten different anatomic presentations and fields of view, resulted in an average root‐mean‐square percentage error of 6.6% for the ten cases studied. The method should be implementable using an array of small lead beam stops placed in the field of only a single mask image and the above described convolution kernel applied to both mask and postopacification images. The mask beam‐stop data are used to scale both mask and postopacification convolution‐filtered images. This scaled, convolution‐filtered image is then subtracted from the original image to produce a largely scatter‐corrected image.