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Neural network regularization in the problem of few-view computed tomography
A.V. Yamaev 1,2, M.V. Chukalina 1,4,3, D.P. Nikolaev 1,3, L.G. Kochiev 5, A.I. Chulichkov 2

Smart Engines Service LLC, Nobelya, 7, Moscow, Russia;
Moscow State University, Michurinsky Pr., 1, Moscow, Russia;
Institute for Information Transmission Problems (Kharkevich Institute) RAS,
Bolshoy Karetny Pereulok,12, stroenie 1, Moscow, Russia;
FSRC "Crystallography and Photonics" RAS, Leninski prospekt, 59, Moscow, Russia;
Simon Fraser University, 8888 University Dr, BC V5A 1S6, Burnaby, Canada

 PDF, 1364 kB

DOI: 10.18287/2412-6179-CO-1035

Страницы: 422-428.

Язык статьи: English.

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.

Ключевые слова:
computed tomography, few-view tomography, artificial intelligence, neural network, U-Net, learned residual fourier reconstruction.

This work was partly supported by RFBR (grants) 18-29-26020 and 19-01-00790.

Yamaev AV, Chukalina MV, Nikolaev DP, Kochiev LG, Chulichkov AI. Neural network regularization in the problem of few-view computed tomography. Computer Optics 2022; 46(3): 422-428. DOI: 10.18287/2412-6179-CO-1035.


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