Tomographic Image Reconstruction based on Artificial Neural Network (ANN) Techniques

Το τεκμήριο παρέχεται από τον φορέα :
Hellenic Nuclear Physics Society   

Αποθετήριο :
Annual Symposium of the Hellenic Nuclear Physics Society  | ΕΚΤ eProceedings   

δείτε την πρωτότυπη σελίδα τεκμηρίου
στον ιστότοπο του αποθετηρίου του φορέα για περισσότερες πληροφορίες και για να δείτε όλα τα ψηφιακά αρχεία του τεκμηρίου*



Tomographic Image Reconstruction based on Artificial Neural Network (ANN) Techniques (EN)

Paschalis, P.
Argyrou, M.
Maintas, D.
Stiliaris, E.

info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

2019-11-23


A new approach for tomographic image reconstruction from projections using Artificial Neural Network (ANN) techniques is presented in this work. The design of the proposed reconstruction system is based on a simple but efficient network architecture, which best utilizes all available input information. Due to the computational complexity, which grows quadratically with the image size, the training phase of the system is characterized by relatively large CPU times. The trained network, on the contrary, is able to provide all necessary information in a quick and efficient way giving results comparable to other time consuming iterative reconstruction algorithms. The performance of the network studied with a large number of software phantoms is directly compared to the well known Algebraic Reconstruction Technique (ART). For a given image and projections size, the role of the hidden layers in the network architecture is examined and the quality dependence of the reconstructed image on the size of the geometrical patterns used in the training phase is also investigated. (EN)


Annual Symposium of the Hellenic Nuclear Physics Society

Αγγλική γλώσσα

Hellenic Nuclear Physics Society (HNPS) (EN)


2654-0088
2654-007X
Annual Symposium of the Hellenic Nuclear Physics Society; Τόμ. 18 (2010): HNPS2010; 89-94 (EL)
HNPS Advances in Nuclear Physics; Vol. 18 (2010): HNPS2010; 89-94 (EN)

Πνευματική ιδιοκτησία (c) 2019 M. Argyrou, P. Paschalis, D. Maintas, E. Stiliaris (EL)




*Η εύρυθμη και αδιάλειπτη λειτουργία των διαδικτυακών διευθύνσεων των συλλογών (ψηφιακό αρχείο, καρτέλα τεκμηρίου στο αποθετήριο) είναι αποκλειστική ευθύνη των αντίστοιχων Φορέων περιεχομένου.