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Proceedings Paper

Neural network scatter correction technique for digital radiography
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Paper Abstract

Ascatter correction technique based on artificial neural networks is presented. The technique utilizes the acquisition of a conventional digital radiographic image, coupled with the acquisition of a multiple pencil beam ("micro-aperture") digital image. Image subtraction results in a sparsely sampled estimate of the scatter component in the image. The neural network is trained to develop a causal relationship between image data on the low-pass filtered open field image and the sparsely sampled scatter image, and then the trained network is used to correct the entire image (pixel by pixel) in a manner which is operationally similar to but potentially more powerful than convolution. The technique is described and is illustrated using clinical "primary" component images combined with scatter component images that are realistically simulated using the results from previously reported Monte Carlo investigations. The results indicate that an accurate scatter correction can be realized using this technique.

Paper Details

Date Published: 1 July 1990
PDF: 10 pages
Proc. SPIE 1231, Medical Imaging IV: Image Formation, (1 July 1990); doi: 10.1117/12.18827
Show Author Affiliations
John M. Boone, Thomas Jefferson Univ. (United States)
James Anthony Seibert, Univ. of California Medical Ctr./Davis (United States)

Published in SPIE Proceedings Vol. 1231:
Medical Imaging IV: Image Formation
Roger H. Schneider, Editor(s)

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