16 April 1996 Artificial neural networks for scatter and attenuation compensation in radioisotope imaging
Author Affiliations +
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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Philippe Maksud, Bernard Fertil, Charles Rica, Andre Aurengo, "Artificial neural networks for scatter and attenuation compensation in radioisotope imaging", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237956; https://doi.org/10.1117/12.237956
PROCEEDINGS
8 PAGES


SHARE
Back to Top