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Proceedings Paper

Artificial neural networks for scatter and attenuation compensation in radioisotope imaging
Author(s): Philippe Maksud; Bernard Fertil; Charles Rica; Andre Aurengo
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Paper Abstract

Conventional nuclear medicine images are spoiled by photon attenuation and scattering. Decreased contrast, blurred object edges and erroneous quantification are the most obvious consequences. Both processes are intimately linked so that a proper correction can hardly be achieved. We have investigated the usefulness of a neural network based approach (ANN) to compensate for these damages. Numerical Monte-Carlo simulations and physical phantoms acquisitions of homogeneous sources of various forms and volumes in a diffusing medium were used to examine these capacities. Using the energy spectrum of incident photons for every pixel of each image and two diametrically opposed views of the radioactive objects as sources of information, a multilayer neural network with backpropagation as learning tool, we were able to get a proper restitution of images so that it seems now possible to run meaningful quantification.

Paper Details

Date Published: 16 April 1996
PDF: 8 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237956
Show Author Affiliations
Philippe Maksud, INSERM (France)
Bernard Fertil, INSERM (France)
Charles Rica, INSERM (France)
Andre Aurengo, INSERM (France)


Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

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