Электронный каталог

Статья

Bakirov, B.
Convolutional Neural Networks for Reconstruction of Neutron Tomography from Incomplete Data / B.Bakirov, S.E.Kichanov, D.P.Kozlenko. – Text : electronic // Nuclear Instruments & Methods in Physics Research B. – 2025. – Vol. 563. – P. 165682. – URL: https://doi.org/10.1016/j.nimb.2025.165682. – Bibliogr.: 67.

To reduce the number of image projections in neutron tomography experiments, tomography reconstruction algorithms are used from an incomplete and limited number of neutron projections. The possibilities of a reconstruction algorithm based on convolutional neural networks are presented. It was found that only 72 projections are required for trained convolutional neural network for a qualitative reconstruction comparable to that from a complete dataset. Variations in the quality of the tomography reconstruction due to changes in training data and the number of input projections are considered. Examples of using convolutional neural networks for neutron tomography reconstructions of real experimental data includes the results of studies on archaeological metal materials from the “Volna-1″ archaeological site.



Спец.(статьи,препринты) = С 325.1а - Нейронные сети и клеточные автоматы
Спец.(статьи,препринты) = С 342 г1 - Замедление и диффузия нейтронов. Дифракция
Спец.(статьи,препринты) = Ц 849 - Искусственный интеллект. Теория и практика
ОИЯИ = ОИЯИ (JINR)2025