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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

1 Smart Engines Service LLC, Nobelya, 7, Moscow, Russia;
2 Moscow State University, Michurinsky Pr., 1, Moscow, Russia;
3 Institute for Information Transmission Problems (Kharkevich Institute) RAS,
Bolshoy Karetny Pereulok,12, stroenie 1, Moscow, Russia;
4 FSRC "Crystallography and Photonics" RAS, Leninski prospekt, 59, Moscow, Russia;
5 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.

Аннотация:
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.

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

Citation:
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.

References:
  1. Chukalina MV, Khafizov AV, Kokhan VV, Buzmakov AV, Senin RA, Uvarov VI, Grigoriev MV. Algorithm for post-processing of tomography images to calculate the dimension-geometric features of porous structures. Computer optics 2021; 45(1): 110-121. DOI: 10.18287/2412-6179-CO-781.
  2. Bulatov K, Chukalina M, Buzmakov A, Nikolaev D, Arlazarov VV. Monitored reconstruction: Computed tomography as an anytime algorithm. IEEE Access 2020; 8: 110759-110774. DOI: 10.1109/ACCESS.2020.3002019.
  3. Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, Kalra MK, Zhang Y, Sun L, Wang G. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans Med Imaging 2018; 37(6): 1348-1357. DOI: 10.1109/TMI.2018.2827462.
  4. Zhu L, Han Y, Li L, Xi X, Zhu M, Yan B. Metal artifact reduction for x-ray computed tomography using U-net in image domain. IEEE Access 2019; 7: 98743-98754. DOI: 10.1109/ACCESS.2019.2930302.
  5. Yamaev A, Chukalina M, Nikolaev D, Sheshkus A, Chulichkov A. Lightweight denoising filtering neural network for FBP algorithm. Proc SPIE 2021; 11605: 116050L. DOI: 10.1117/12.2587185.
  6. Yang H-K, Liang K-C, Kang K-J, Xing Y-X. Slice-wise reconstruction for low-dose cone-beam CT using a deep residual convolutional neural network. Nucl Sci Tech 2019; 30(4): 59. DOI: 10.1007/s41365-019-0581-7.
  7. Adler J, Öktem O. Learned primal-dual reconstruction. IEEE transactions on Medical Imaging 2018; 37(6): 1322-1332. DOI: 10.1109/TMI.2018.2799231.
  8. Nakai H, Nishio M, Yamashita R, Ono A, Nakao KK, Fujimoto K, Togashi K. Quantitative and qualitative evaluation of convolutional neural networks with a deeper u-net for sparse-view computed tomography reconstruction. Acad Radiol 2020; 27(4): 563-574.
  9. Kang E, Ye JC, et al. Wavelet domain residual network (WavResNet) for low-dose X-ray CT reconstruction. arXiv preprint, 2017. Source: <https://arxiv.org/abs/1703.01383>.
  10. Harms J, Lei Y, Wang T, Zhang R, Zhou J, Tang X, Curran WJ, Liu T, Yang X. Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography. Med phys 2019; 46(9): 3998-4009. DOI: 10.1002/mp.13656.
  11. McCollough CH, Bartley AC, Carter RE, Chen B, Drees TA, Edwards P, R Holmes III DR, Huang AE, Khan F, Leng S, McMillan KL, Michalak GJ, Nunez KM, Yu L, Fletcher JG. Low-dose CT for the detection and classification of metastatic liver lesions: Results of the 2016 low dose CT grand challenge. Med Phys 2017; 44(10): e339-e352. DOI: 10.1002/mp.12345.
  12. Pyts’ev YuP. Measurement-computation converter as a measurement facility. Autom Remote Control 2010; 71(2): 303-319. DOI: 10.1134/S0005117910020116.
  13. Buzmakov A, Zolotov D, Chukalina M, Ingacheva A, Sheshkus A, Asadchikov V. Iterative tomography reconstruction in a limited field of view. Proc SPIE 2020; 11433: 114331W. DOI: 10.1117/12.2557501.
  14. Usanov MS, Kulberg NS, Morozov SP. Usage of adaptive homomorphic filters for CT processing [In Russian]. Informatsionnye Tekhnologii i Vychslitel'nye Sistemy 2017; 2: 33-42.
  15. Wei Y, Wang G, Hsieh J. An intuitive discussion on the ideal ramp filter in computed tomography (I). Comput Math with Appl 2005; 49(5-6): 731-740. DOI: 10.1016/j.camwa.2004.10.034.
  16. Su BL, Zhang YH, Peng LH, Yao DY, Zhang BF. The use of simultaneous iterative reconstruction technique for electrical capacitance tomography. Chem Eng J 2000; 77(1-2): 37-41. DOI: 10.1016/S1385-8947(99)00134-5.
  17. McCollough C. TU-FG-207A-04: Overview of the low dose CT grand challenge. Med Phys 2016; 43(6Part35): 3759-3760. DOI: 10.1118/1.4957556.
  18. Devaney AJ. Generalized projection-slice theorem for fan beam diffraction tomography. Ultrasonic Imaging 1985; 7(3): 264-275. DOI: 10.1016/0161-7346(85)90006-9.
  19. Gotmare A, Keskar NS, Xiong C, Socher R. A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation. arXiv preprint, 2018. Source: <https://arxiv.org/abs/1810.13243>.
  20. Soshin KV, Nikolaev DP, Gladilin SA, Ershov EI. Acceleration of summation over segments using the fast hough transformation pyramid [In Russian]. Vestnik Yuzhno-Ural'skogo Universiteta. Seriya Matematicheskoe Modelirovanie i Programmirovanie 2020; 13(1): 129-140. DOI: 10.14529/mmp200110.

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