Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Pre-reconstruction restoration of SPECT projection images by a neural network

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

2 Author(s)
Sanjay Gopal, S. ; Dept. of Electr. Eng., Houston Univ., TX, USA ; Hebert, T.J.

In single photon emission computed tomography (SPECT) the projection images obtained at view angles surrounding the patient are degraded due to the geometric response of the imaging system (a spatially-variant blur), Compton scatter, Poisson noise, and other factors. Various methods have been proposed for compensating for the spatially varying geometric response of the camera. Here the authors examine restoration of SPECT projection images using an artificial neural network. A three layer feedforward neural network is trained to compute the spatially-variant standard deviations of a symmetric Gaussian blur. A Hopfield network is then used to restore the projection images in which the restoration problem is formulated as a minimization of an error function of the network. Results from applying this restoration procedure on SPECT projection images are presented and the resulting SPECT reconstructions are analysed

Published in:

Nuclear Science, IEEE Transactions on  (Volume:41 ,  Issue: 4 )