(48-4) 15 * << * >> * Russian * English * Content * All Issues

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

Pages: 601-609.

Full text of article: English language.

Abstract:
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.

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

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.

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

References:
  1. Bulatov K, Chukalina M, Buzmakov A, Nikolaev D, Arlazarov V. Monitored reconstruction: Computed tomography as an anytime algorithm. IEEE Access 2020; 8: 110759-110774. DOI: 10.1109/ACCESS.2020.3002019.
  2. McCollough C. TU-FG-207A-04: Overview of the low dose CT grand challenge. Med Phys 2016; 43: 3759-3760. DOI: 10.1118/1.4957556.
  3. Yu W, Zeng L. A novel weighted total difference based image reconstruction algorithm for few-view computed tomography. PLoS One 2014; 9: e109345. DOI: 10.1371/journal.pone.0109345.
  4. Hanson K, Wecksung G. Bayesian approach to limited-angle reconstruction in computed tomography. J Opt Soc Am 1983; 73(11): 1501-1509. DOI: 10.1364/JOSA.73.001501.
  5. Nguyen, L. How strong are streak artifacts in limited angle computed tomography? Inverse Probl 2015; 31(5): 055003. DOI: 10.1088/0266-5611/31/5/055003.
  6. Yu W, Wang C, Huang M. Edge-preserving reconstruction from sparse projections of limited-angle computed tomography using l 0-regularized gradient prior. Rev Sci Instrum 2017; 88: 043703 DOI: 10.1063/1.4981132.
  7. Marshall H, Bowman R, Yang I, Fong K, Berg C. Screening for lung cancer with low-dose computed tomography: a review of current status. J Thorac Dis 2013; 5: S524. DOI: 10.3978/j.issn.2072-1439.2013.09.06.
  8. Wenholz A, Okereke I. Lung cancer screening using low-dose computed tomography. In Book: El-Baz A, Suri J, eds. Lung imaging and CADx. Ch 5. Boca Raton: CRC Press; 2019. DOI: 10.1201/9780429055959-5.
  9. Leuschner J, Schmidt M, Baguer D, Maass P. LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction. Sci Data 2021; 8, 109. DOI: 10.1038/s41597-021-00893-z.
  10. Smith-Bindman R, et al. Is computed tomography safe? N Engl J Med 2010; 363: 1-4. DOI: 10.1056/NEJMp1002530.
  11. Giantsoudi D, De Man B, Verburg J, Trofimov A, Jin Y, Wang G, Gjesteby L, Paganetti H. Metal artifacts in computed tomography for radiation therapy planning: dosimetric effects and impact of metal artifact reduction. Phys Med Biol 2017; 62: R49. DOI: 10.1088/1361-6560/aa5293.
  12. Shikhaliev P. Beam hardening artefacts in computed tomography with photon counting, charge integrating and energy weighting detectors: a simulation study. Phys Med Biol 2005; 50: 5813. DOI: 10.1088/0031-9155/50/24/004.
  13. Joseph P, Spital R. The effects of scatter in x-ray computed tomography. Med Phys 1982; 9: 464-472. DOI: 10.1118/1.595111.
  14. Chen G, Kung J, Beaudette K. Artifacts in computed tomography scanning of moving objects. Semin Radiat Oncol 2004; 14: 19-26. DOI: 10.1053/j.semradonc.2003.10.004.
  15. Tian C, Fei L, Zheng W, Xu Y, Zuo W, Lin C. Deep learning on image denoising: An overview. arXiv Preview. 2020. Source: <https://arxiv.org/abs/1912.13171>. DOI: 10.48550/arXiv.1912.13171.
  16. Kappeler A, Yoo S, Dai Q, Katsaggelos A. Video super-resolution with convolutional neural networks. IEEE Trans Comput Imaging 2016; 2: 109-122. DOI: 10.1109/TCI.2016.2532323.
  17. Xie J, Xu L, Chen E. Image denoising and inpainting with deep neural networks. Proc 25th Int Conf on Neural Information Processing Systems (NIPS'12) 2012; 1: 341-349.
  18. Hu D, Zhang Y, Quan G, Xiang J, Coatrieux G, Luo S, Coatrieux J, Ji X, Han H, Chen Y. CROSS: Cross-domain residual-optimization based structure strengthening reconstruction for limited-angle CT. IEEE Trans Radiat Plasma Med Sci 2023; 7(5): 521-531. DOI: 10.1109/TRPMS.2023.3242662.
  19. Li Q, Li R, Li S, Wang T, Cheng Y, Zhang S, Wu W, Zhao J, Qiang Y, Wang L. Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network. Med Phys 2023; 51(2): 1289-1312. DOI: 10.1002/mp.16331.
  20. Ma Y, Ren Y, Feng P, He P, Guo X, Wei B. Sinogram denoising via attention residual dense convolutional neural network for low-dose computed tomography. Nucl Sci Tech 2021; 32: 41. DOI: 10.1007/s41365-021-00874-2.
  21. Adler J, Öktem O. Learned primal-dual reconstruction.  IEEE Trans Med Imaging 2018; 37: 1322-1332. DOI: 10.1109/TMI.2018.2799231.
  22. Ghadrdan S, Alirezaie J, Dillenseger J, Babyn P. Low-dose computed tomography image denoising based on joint wavelet and sparse representation. 36th Annual Int Conf of the IEEE Engineering In Medicine And Biology Society 2014: 3325-3328. DOI: 10.1109/EMBC.2014.6944334.
  23. Moen T, Chen B, Holmes III D, Duan X, Yu Z, Yu L, Leng S, Fletcher J, McCollough C. Low-dose CT image and projection dataset. Med Phys 2021; 48: 902-911. DOI: 10.1002/mp.14594.
  24. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013; 26: 1045-1057. DOI: 10.1007/s10278-013-9622-7.
  25. McCollough C, Chen B, Holmes D, Duan X, Yu Z, Xu L, Leng S, Fletcher J. Low dose ct image and projection data [data set]. The Cancer Imaging Archive 2020; 10. DOI: 10.1002/mp.14594.
  26. Van Aarle W, Palenstijn W, Cant J, Janssens E, Bleichrodt F, Dabravolski A, De Beenhouwer J, Batenburg K, Sijbers J. Fast and flexible X-ray tomography using the ASTRA toolbox. Opt Express 2016; 24(22): 25129-25147. DOI: 10.1364/OE.24.025129.
  27. Falcon W. Pytorch lightning. GitHub. 3 (2019)
  28. Jin K, McCann M, Froustey E, Unser M. Deep convolutional neural network for inverse problems in imaging. IEEE Trans Image Process 2017; 26: 4509-4522. DOI: 10.1109/TIP.2017.2713099.
  29. 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.
  30. Smolin A, Yamaev A, Ingacheva A, Shevtsova T, Polevoy D, Chukalina M, Nikolaev D, Arlazarov V. Reprojection-based numerical measure of robustness for CT reconstruction neural network algorithms. Mathematics 2022; 10: 4210. DOI: 10.3390/math10224210.
  31. Pan X, Sidky E, Vannier M. Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? Inverse Problems 2009; 25: 123009. DOI: 10.1088/0266-5611/25/12/123009.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20