DocumentCode
768258
Title
Approximate reconstruction of PET data with a self-organizing neural network
Author
Comtat, C. ; Morel, C.
Author_Institution
Inst. de Phys. Nucleaire, Lausanne Univ., Switzerland
Volume
6
Issue
3
fYear
1995
fDate
5/1/1995 12:00:00 AM
Firstpage
783
Lastpage
789
Abstract
Self-organization was observed using the algorithm of Kohonen with an original “distance” adapted to stimuli resulting from coincident detections of electron-positron annihilation photon pairs. This has led to a method for approximate reconstruction of two-dimensional positron emission tomography (2-D PET) images that is totally independent of the number of detectors. To obtain meaningful information about the distribution of the radioactive tracer, a toroidal architecture must be used for the network
Keywords
biomedical imaging; image reconstruction; medical image processing; positron emission tomography; self-organising feature maps; 2-D PET images; approximate reconstruction; electron-positron annihilation photon pairs; self-organizing neural network; two-dimensional positron emission tomography images; Cognitive science; Computer networks; Detectors; Econometrics; Equations; Image reconstruction; Neural networks; Positron emission tomography; Random number generation; Recurrent neural networks;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.377988
Filename
377988
Link To Document