A 2D approach to tomographic image reconstruction using a Hopfield-type neural network.
OBJECTIVE
In this paper a new approach to tomographic image reconstruction from projections is developed and investigated.


METHODS
To solve the reconstruction problem a special neural network which resembles a Hopfield net is proposed. The reconstruction process is performed during the minimizing of the energy function in this network. To improve the performance of the reconstruction process an entropy term is incorporated into energy expression.


CONCLUSIONS
The approach presented in this paper significantly decreases the complexity of the reconstruction problem.
Cierniak
Robert
R
Technical University of Czestochowa, Department of Computer Engineering, Armii Krajowej Avenue 36, Pl.-42-200 Czestochowa, Silesia, Poland. cierniak@kik.pcz.czest.pl
D000465:Algorithms; D003198:Computer Simulation; D019277:Entropy; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012886:Skull / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed