Applied Neural Networks For Signal Processing
Applied Neural Networks For Signal Processing
Vector-entropy optimization-based neural-network approach to image reconstruction from projections
IEEE Transactions on Neural Networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
A neural network optimization-based method of image reconstruction from projections
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Image reconstruction by an alternating minimisation
Neurocomputing
An analytical approach to the image reconstruction problem using EM algorithm
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
A neuronal approach to the statistical image reconstruction from projections problem
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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Neural networks have some applications in computerized tomography, in particular to reconstruct an image from projections. The presented paper describes a new practical approach to the reconstruction problem using a Hopfield-type neural network. The methodology of this reconstruction algorithm resembles a transformation formula-the so-called @r-filtered layergram method. The method proposed in this work is adapted for discrete fan beam projections, already used in practice. Performed computer simulations show that the neural network reconstruction algorithm designed to work in this way outperforms conventional methods in obtained image quality, and in perspective of hardware implementation in the speed of the reconstruction process.