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Monitored reconstruction improved by post-processing neural network
A.V. Yamaev 1,2

1 Moscow State University, Russia, 119991, Moscow, Leninskie Gory, 1;
2 Smart Engines Service LLC, Russia, Moscow,
119234, Moscow, Russia, 60th Anniversary of October Avenue 9

  PDF, 2773 kB

DOI: 10.18287/2412-6179-CO-1389

Страницы: 601-609.

Язык статьи: English.

Аннотация:
Computed tomography (CT) is widely utilized for analyzing internal structures, but the limitations of traditional reconstruction algorithms, which often require a large number of projections, restrict their effectiveness in time-critical tasks or for biological objects studying. Recently Monitored reconstruction approach was proposed for reducing the requirement of dose load. In this paper, there were investigated the advantages of using post-processing neural networks within a monitored reconstruction approach. Three algorithms, namely FBP, FBPConvNet, and LRFR, are evaluated based on their mean count of projections required for the achievement of target reconstruction accuracy. A novel training method specifically designed for neural network algorithms within the Monitored reconstruction framework is proposed. It is shown that the use of the LRFR approach allows one to achieve both a reduction in the number of measured projections and an improvement in the reconstruction accuracy over a certain range of stopping rules. These findings highlight the significant potential of neural networks to be used in the Monitored reconstruction approach.

Ключевые слова:
monitored reconstruction, few-view, computed tomography, x-ray, deep learning, post-processing neural network.

Благодарности
The work was partly funded by the Russian Science Foundation under grant 23-21-00524.

Citation:
Yamaev AV. Monitored reconstruction improved by post-processing neural network. Computer Optics 2024; 48(4): 601-609. DOI: 10.18287/2412-6179-CO-1389.

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