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Volume 36 #2

Contents

  1. Component N2pc in Studying Visual Attention
  2. Conceptual Model of Sensor System Ontology with Event Information Processing Method
  3. Regeneration of the Helix lucorum visual body in a 185-day orbital flight et the International Space Station
  4. Multiplicatively Сlosed Spectral Models in Color Analysis
  5. About Monitored Tomographic Reconstruction

About Monitored Tomographic Reconstruction

© 2022 М. V. Chukalinaa,b, А. S. Ingachevab,c, K. B. Bulatovb,d, K. О. Kutukovae, E. Zscheche, V. V. Arlazarovb,d

aFSRC Cristallograhy and photonics 119333 Moscow, Leninskii prospect, 59, Russia
bSmart Engines Service 117312 Moscow, pr. 60-letiya Oktyabrya, 9, Russia
cInstitute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute) 127051 Moscow, Bolshoi karetnii., 19, Russia
dFederal Research Center “Computer Science and Control” of Russian Academy of Sciences 117312 Moscow, pr. 60-letiya Oktyabrya, 9, Russia
edeepXscan GmbH, Dresden, Germany

Received 14 Jan 2022

X-ray tomography is widely used in medicine, industry, customs control and, of course, in scientific research studies as a non-destructive method for visualizing the internal morphological structure of probed objects. Each application imposes its own limitations on the method. Thus, medical applications require limiting the dose load, and use at customs requires a reduction in inspection time. Recently, the authors proposed a fundamentally new approach to working with tomographic data, called monitored reconstruction. The proposed approach differs from the classical two-stage “projection collecting according to a given protocol and then reconstruction”. In monitored approach the reconstruction of a digital image begins after the first few projections (package of projections) are taken, the next step is to analyze the intermediate result and automatically decide to continue measuring the next “package” of projections or consider the result as final, and stop the process. The article discusses in detail the basic principles of the monitored approach, determines the function of total losses, the cost of reconstruction error, the cost of observations. The parameters that affect the formula of the observation cost function depend on the field of method application. The results of a model monitored experiment with projections collected on a tomographic setup with nanometer resolution were analyzed. It is shown that the application of the monitored reconstruction approach to the data allowed us to reduce the number of required projections by 10% on average to achieve a 5% deviation from the “exact” answer, compared with the case of the classical two-stage approach.

Key words: X-ray tomography, two-stage reconstruction, monitored reconstruction, cost of observation, total loss function

DOI: 10.31857/S0235009222020032

Cite: Chukalina М. V., Ingacheva А. S., Bulatov K. B., Kutukova K. О., Zschech E., Arlazarov V. V. O monitoringovom podkhode k tomograficheskoi rekonstruktsii [About monitored tomographic reconstruction]. Sensornye sistemy [Sensory systems]. 2022. V. 36(2). P. 183–193 (in Russian). doi: 10.31857/S0235009222020032

References:

  • Decree of the Chief State Sanitary Doctor of the Russian Federation of July 7, 2009 № 47 “On approval of SanPiN 2.6.1.2523-09” (together with “NRB-99/2009. SanPiN 2.6.1.2523-09. Radiation safety standards. Sanitary rules and regulations”) (Registered in the Ministry of Justice of the Russian Federation on August 14. 2009 N 14534) (in Russia).
  • Gladilin S., Kotov A., Nikolaev D., Usilin S. Postroenie ustoychivykh priznakov detektsii i klassifikatsii obektov, ne obladayuschikh kharakternymi yarkostnymi kontrastami [Construction of robust features for detection and classification of objects without characteristic brightness contrasts]. J. Inform. Тechnol. Comp. Systems. 2014. № 1. P. 53–60 (in Russia).
  • Naterrer F. Matematicheskie aspekti komputernoi tomografii [Mathematical aspects of computed tomography]. М.: Mir, 1990. 105 p. (in Russia).
  • Simonov E.N., Avramov M.V., Avramov D.V. Analiz trehmernih algoritmov rekonstruktsii v rentgenovskoi komputernoi tomografii [Comparison of 3D Reconstruction Algorithm in X-Ray Computed Tomography]. Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control, Radio Electronics. 2017. V. 17. N 2. P. 24–32 (in Russia). https://doi.org/10.14529/ctcr170202
  • Hofer М. Kompyternay tomografiy [Computed tomography]. Basic guide. 3 ed.. М.: Medical literature. 2010. 232 p.
  • Arlazarov V.L., Nikolaev D.P., Arlazarov V.V., Chukalina M.V. X-ray tomography: the way from layer-by-layer radiography. Computer Optics. 2021. V. 45 (6). P. 897–906. https://doi.org/10.18287/2412-6179-CO-898
  • De Andrade V., Nikitin V., Wojcik M., Deriy A., Bean S., Shu D., Mooney T., Peterson K., Kc P., Li K., Ali S., Fezzaa K., Gürsoy D., Arico C., Ouendi S., Troadec D., Simon P., De Carlo F., Lethien C. Fast X-ray Nanotomography with Sub-10 nm Resolution as a Powerful Imaging Tool for Nanotechnology and Energy Storage Applications. Adv. Mater. 2021. V. 33 (21): e2008653. Epub 2021 Apr 19. https://doi.org/10.1002/adma.20200865333871108
  • Bulatov K., Razumnyi N., Arlazarov V.V. On optimal stopping strategies for text recognition in a video stream as an application of a monotone sequential decision model. IJDAR. 2019. V. 22 (3). P. 303–314. https://doi.org/10.1007/s10032-019-00333-0
  • Bulatov K., Chukalina M., Buzmakov A., Nikolaev D., Arlazarov V.V. Monitored Reconstruction: Computed Tomography as an Anytime Algorithm. IEEE Access. 2020. V. 8. P. 110759–110774. https://doi.org/10.1109/ACCESS.2020.3002019
  • Buzung T.M. Computed Tomography. From photon statistics to modern cone-beam CT. Springer-Verlag Berlin Heidelberg. 2008. 521 p.
  • Dabli D., Frandon J., Belaouni A., Akessoul P., Addala T., Berny L., Beregi J-P., Greffier J. Optimization of image quality and accuracy of low iodine concentration quantification as function of dose level and reconstruction algorithm for abdominal imaging using dual-source CT: A phantom study. Diagnostic and Interventional Imaging. 2021. V. 103 (1). P. 31–40. https://doi.org/10.1016/j.diii.2021.08.004
  • Dixon R.L. A new look at CT dose measurement: Beyond CTDI. Medical Physics. 2003. V. 30 (6). P. 1272–1280. https://doi.org/10.1118/1.1576952
  • Ferguson T.S. Optimal stopping and applications. 2008. [Online]. Available: https://www.math.ucla.edu/~tom/Stopping
  • Little M.P., Patel A., Lee C., Hauptmann M., Berrington de Gonzalez A., Albert P., Impact of Reverse Causation on Estimates of Cancer Risk Associated With Radiation Exposure From Computerized Tomography: A Simulation Study Modeled on Brain Cancer. American Journal of Epidemiology. 2022. V. 191 (1). P. 173–181. https://doi.org/10.1093/aje/kwab247
  • Manerikar A., Li F., Kak A.C. DEBISim: A simulation pipeline for dual energy CT-based baggage inspection systems. Journal of X-Ray Science and Technology. 2021. V. 29 (2). P. 259–285. https://doi.org/10.3233/XST-200808
  • Mitsuyama Y., Katayama Y., Oi K., Shimazaki Ji., Mimura K., Endo M., Shimazu N. The accuracy of contrast-enhanced computed tomography scans to detect postpartum haemorrhage: an observational study. BMC Pregnancy Childbirth. 2022. V. 22 (67). P. 1–9. https://doi.org/10.1186/s12884-021-04306-2
  • Nikolaev D.P., Buzmakov A., Chukalina M., Yakimchuk I., Gladkov A., Ingacheva A. “CT Image Quality Assessment based on Morphometric Analysis of Artifacts”. SPIE 10253. 2016. P.10253–06. https://doi.org/10.1117/12.2266268
  • Nourazar M., Goossens B. Accelerating iterative CT reconstruction algorithms using Tensor Cores. Journal RealTime Image Processing. 2021. N. 18. P. 1979–1991. https://doi.org/10.1007/s11554-020-01069-5
  • Ota J., Yokota H., Kobayashi T., Ogata Yu., Kubo T., Chida K., Masuda Yo., Uno T. Head CT dose reduction with organ-based tube current modulation. Medical Physics. 2022. https://doi.org/10.1002/mp.15467
  • Prodi E., Danieli L., Manno C., Pagnamenta A., Pravatà E., Roccatagliata L., Städler S., Cereda C.W., Cianfoni A. Stroke Mimics in the Acute Setting: Role of Multimodal CT Protocol. American Journal of Neuroradiology. 2021. https://doi.org/10.3174/ajnr.A7379
  • Riis N.A., Frosig J., Dong Y., Hansen P.S. Limited-data X-ray CT for underwater pipeline inspection. Inverse Problems. 2018. V. 34 (3). P. 034002. https://doi.org/10.1088/1361-6420/aaa49c
  • Riis N.A.B., Dong Yi., Hansen P.C. Computed tomography with view angle estimation using uncertainty quantification. Inverse Problems. 2021. V. 37 (6). P. 065007. https://doi.org/10.1088/1361-6420/abf5ba
  • Topal E., Liao Z., Loeffler M., Gluch J., Zhang J., Feng X., Zschech E. Multi–scale X–ray Tomography and Machine Learning Algorithms to Study MoNi4 Electrocatalysts Anchored on MoO2 Cuboids Aligned on Ni Foam. BMC Materials. 2020. V. 2 (5). P. 1–14. https://doi.org/10.1186/s42833-020-00011-0
  • Zhang J., Wang T., Liu P., Liao Z., Liu S., Zhuang X., Chen M., Zschech E., Feng X. Efficient Hydrogen Production on MoNi4 Electrocatalysts with Fast Water Dissociation Kinetics. Nature Communications. 2017. V. 8 (1547). P. 1–8. https://doi.org/10.1038/ncomms15437
  • Zilberstein S. Using anytime algorithms in intelligent systems. AI Magazine. 1996. V. 17 (3). P. 73–83. https://doi.org/10.1609/aimag.v17i3.1232