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Computer Optics, 2022, Volume 46, Issue 3, Pages 422–428
DOI: https://doi.org/10.18287/2412-6179-CO-1035
(Mi co1031)
 

This article is cited in 6 scientific papers (total in 6 papers)

INTERNATIONAL CONFERENCE ON MACHINE VISION

Neural network regularization in the problem of few-view computed tomography

A. V. Yamaevab, M. V. Chukalinaacd, D. P. Nikolaevad, L. G. Kochieve, A. I. Chulichkovb

a Smart Engines Service LLC, Moscow
b Moscow State University, Michurinsky Pr., 1, Moscow, Russia
c FSRC "Crystallography and Photonics" RAS
d Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Moscow
e Simon Fraser University
Abstract: The computed tomography allows to reconstruct the inner morphological structure of an object without physical destructing. The accuracy of digital image reconstruction directly depends on the measurement conditions of tomographic projections, in particular, on the number of recorded projections. In medicine, to reduce the dose of the patient load there try to reduce the number of measured projections. However, in a few-view computed tomography, when we have a small number of projections, using standard reconstruction algorithms leads to the reconstructed images degradation. The main feature of our approach for few-view tomography is that algebraic reconstruction is being finalized by a neural network with keeping measured projection data because the additive result is in zero space of the forward projection operator. The final reconstruction presents the sum of the additive calculated with the neural network and the algebraic reconstruction. First is an element of zero space of the forward projection operator. The second is an element of orthogonal addition to the zero space. Last is the result of applying the algebraic reconstruction method to a few-angle sinogram. The dependency model between elements of zero space of forward projection operator and algebraic reconstruction is built with neural networks. It demonstrated that realization of the suggested approach allows achieving better reconstruction accuracy and better computation time than state-of-the-art approaches on test data from the Low Dose CT Challenge dataset without increasing reprojection error.
Keywords: computed tomography, few-view tomography, artificial intelligence, neural network, U-Net, learned residual fourier reconstruction
Funding agency Grant number
Russian Foundation for Basic Research 18-29-26020
19-01-00790
This work was partly supported by RFBR (grants) 18-29-26020 and 19-01-00790.
Received: 01.09.2021
Accepted: 17.01.2021
Document Type: Article
Language: English
Citation: A. V. Yamaev, M. V. Chukalina, D. P. Nikolaev, L. G. Kochiev, A. I. Chulichkov, “Neural network regularization in the problem of few-view computed tomography”, Computer Optics, 46:3 (2022), 422–428
Citation in format AMSBIB
\Bibitem{YamChuNik22}
\by A.~V.~Yamaev, M.~V.~Chukalina, D.~P.~Nikolaev, L.~G.~Kochiev, A.~I.~Chulichkov
\paper Neural network regularization in the problem of few-view computed tomography
\jour Computer Optics
\yr 2022
\vol 46
\issue 3
\pages 422--428
\mathnet{http://mi.mathnet.ru/co1031}
\crossref{https://doi.org/10.18287/2412-6179-CO-1035}
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  • https://www.mathnet.ru/eng/co/v46/i3/p422
  • This publication is cited in the following 6 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
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