Abstract. 

Monitored tomographic reconstruction (MTR) is a novel approach of dose reduction in computed tomography (CT), which also allows to reduce experiment time in micro tomography setups. The core of MTR is simultaneous projection data registration and its reconstruction with the stop of acquisition process after some predefined quality has been achieved. The order of projection acquisition is defined in the scanning protocol by the means of calibration on the test objects of a similar structure and under similar experimental conditions. The MTR is able to achieve a fixed average reconstruction quality with lower average dose/projection count than the conventional CT protocols allows. In the present work we study MTR on the synthetic data for three different reconstruction algorithms (FBP, SIRT, SIRT-TV) and three different image quality metrics. We found that even though the optimal projections count under predefined quality is algorithm and metric dependent, the general idea still holds, and the gain of MTR over conventional protocol can be achieved.

Keywords: 

computed tomography, monitored tomography reconstruction, reconstruction algorithm, quality function, dose reduction, stopping rule.

PP. 10-18.

DOI: 10.14357/20790279220302

 

References

1. Kak A.C., Slaney M. Principles of computerized tomographic imaging. Society for Industrial and Applied Mathematics. 2001.

2. Bulatov K.B., Chukalina M.V., Nikolaev D.P. Fast X-ray sum calculation algorithm for computed tomography problem // Bulletin of the South Ural State University. Ser. Mathematical Modelling, Programming & Computer Software. 2020. V. 13. No. 1. P. 95-106.

3. Pashina T.A. et al. Automatic highlighting of the region of interest in computed tomography images of the lungs //Computer Optics. 2020. V. 44. No. 1. P. 74-81.

4. Baldacci F. et al. 3D human airway segmentation from high hesolution MR imaging //Eleventh International Conference on Machine Vision (ICMV 2018). SPIE, 2019. V. 11041. P. 244-250.

5. Sharma M., Bhatt J.S., Joshi M.V. Early detection of lung cancer from CT images: nodule segmentation and classification using deep learning //Tenth International Conference on Machine Vision (ICMV 2017). International Society for Optics and Photonics, 2018. V. 10696. P. 106960W.

6. Bartscher M., Neuschaefer-Rube U., Wäldele F. Computed Tomography – a highly potential tool for Industrial quality control and production near measurement// 8th International Symposium on Measurement and Quality Control in Production, Erlangen, Germany. 2004.

7. Topal E. et al. 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. No. 1. P. 1–14.

8. Polikarpov M. et al. Visualization of protein crystals by high-energy phase-contrast X-ray imaging //Acta Crystallographica Section D: Structural Biology. 2019. V. 75. No 11. P. 947-958.

9. Porosity estimation method by X-ray computed tomography. – Available at: https://www.sciencedirect.

com/science/article/pii/S0920410505000586 (accessed 19.11.2021).

10. Prokop M., Galansky M. Spiral and multilayer computed tomography //Study guide. [Spiral’naya i mnogoslojnaya komp’yuternaya tomografiya //Uchebnoe posobie.] 2007. V. 2.

11. Brenner D.J., Hall E.J. Computed tomography - an increasing source of radiation exposure //New England journal of medicine. 2007. V. 357. No 22. P. 2277-2284.

12. Matkevich E.I., Sinitsyn V.E., Mershina E.A. Comparative analysis of radiation doses of patients with computed tomography in a federal medical institution //Bulletin of Radiology and Radiology. [Sravnitel’nyj analiz doz oblucheniya pacientov pri komp’yuternoj tomografii v federal’nom lechebnom uchrezhdenii //Vestnik rentgenologii i radiologii.] 2016. Т. 97. №. 1. P. 33-39.

13. Matkevich E.I., Ivanov I.V. Directions of radiation dose reduction in computed tomography [Napravleniya snizheniya dozy oblucheniya pri komp’yuternoj tomografii] //Research’n Practical Medicine Journal. 2018. V. 5. No Special Issue 2. P. 48-49.

14. Slovis T. L. The ALARA concept in pediatric CT: myth or reality? //Radiology. 2002. V. 223. No 1. P. 5-6.

15. Mittal T.K., Rubens M.B. Computed tomography techniques and principles. Part A. Electron beam computed tomography // Noninvasive Imaging of Myocardial Ischemia. Springer, London. 2006. P. 93-98.

16. MacGregor K. et al. Identifying institutional diagnostic reference levels for CT with radiation dose index monitoring software //Radiology. 2015. V. 276. No 2. P. 507-517.

17. Karmazanovsky G.G. MSCT with low radiation load in the visualization of neoplasms of the liver and pancreas [MSKT s nizkoj luchevoj nagruzkoj v vizualizacii novoobrazovanij pecheni i podzheludochnoj zhelezy].

18. Likhachev A.V. Increasing the contrast of low-grade tomograms obtained by algebraic reconstruction algorithms //Computing technologies. [Povyshenie kontrastnosti malorakursnyh tomogramm, poluchennyh algebraicheskimi algoritmami rekonstrukcii //Vychislitel’nye tekhnologii.] 2009. V. 14. No 3. P. 37-47.

19. Bulatov K. et al. Monitored reconstruction: Computed tomography as an anytime algorithm //IEEE Access. 2020. V. 8. P. 110759-110774.

20. Buzug T.M., Thorsten M. Computed tomography // Springer handbook of medical technology. Springer, Berlin, Heidelberg. 2011. P. 311-342.

21. Basu S., Bresler Y. Filtered backprojection reconstruction algorithm for tomography //IEEE Transactions on Image Processing. 2000. V. 9. No 10. P. 1760-1773.

22. Gordon R., Bender R., Herman G.T. Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography //Journal of theoretical Biology. 1970. V. 29. No 3. P. 471-481.

23. Sidky E.Y., Pan X. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization //Physics in Medicine & Biology. 2008. V. 53. No 17. P. 4777.

24. Chai T., Draxler R.R. Root mean square error (RMSE) or mean absolute error (MAE) //Geoscientific Model Development Discussions. 2014. V. 7. No 1. P. 1525-1534.

25. Shcherbakov M.V. et al. Overview of forecast error indicators [Obzor pokazatelej oshibok prognoza] // World applied sciences journal. 2013. 24(24). P. 171-176.

26. Garcia-Ramirez J.L. et al. Performance evaluation of an 85-cm-bore X-ray computed tomography scanner designed for radiation oncology and comparison with current diagnostic CT scanners //International Journal of Radiation Oncology* Biology* Physics. 2002. V. 52. No 4. P. 1123-1131.

27. Mansour Z. et al. Quality control of CT image using American College of Radiology (ACR) phantom // The Egyptian journal of Radiology and nuclear medicine. 2016. V. 47. No 4. P. 1665-1671.

28. Correia J.A. A Novel High Resolution Positron Emission Tomography System for Measurement of Bone Metabolism. Massachusetts general hospital Boston. 2003.

29. Faragó T. et al. syris: a flexible and efficient framework for X-ray imaging experiments simulation // Journal of Synchrotron Radiation. 2017. V. 24. No 6. P. 1283-1295.

30. Description of the smart tomo engine software. Available at: https://smartengines.com/ocr-engines/tomo-engine (accessed 19.05.22).