Cart (Loading....) | Create Account
Close category search window
 

Reconstruction of Compton-camera images using artificial neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Karg, T. ; Phys. Inst. IV, Friedrich-Alexander-Univ. Erlangen-Nurnberg, Erlangen, Germany ; Pauli, J. ; Anton, G. ; Beulertz, W.

We report on a new and fast approach for the three-dimensional reconstruction of X-ray source distributions monitored by a Compton-camera consisting of two layers of pixel-detectors. We use multi-layer feedforward artificial neural networks trained by a standard backpropagation algorithm to determine the probability that a given Compton scattered photon originated from a certain point in the reconstruction space. Summing up this probability for all measured photons at each volume element (voxel) of the discretized reconstruction space gives a good estimate of the real X-ray source distribution. We reconstruct and discuss the point spread function obtained with different materials (germanium, silicon) used for the scatter detector and incident photons having an energy of 122 keV and 512 keV

Published in:

Nuclear Science Symposium Conference Record, 2001 IEEE  (Volume:4 )

Date of Conference:

2001

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.