Data mining and image recognition
Intellectual systems and technologies
Image and signal processing
V.E. Prun Reducing the influence of high-absorbing inclusions on CT reconstructions using algebraic reconstruction technique
MACHINE LEARNING
V.E. Prun Reducing the influence of high-absorbing inclusions on CT reconstructions using algebraic reconstruction technique

Abstract.

The presence of high absorbing inclusions, e.g. metals, causes appearance of artifacts on CT reconstructed images. This paper treats ways of suppressing such artifacts. A constrained optimization problem is stated within the formulated artifact model. The problem is solved on input images of size 64x64 pixels, using the quadratic additive penalties and using the logarithmic barrier functions. The reconstructions are performed using modelled data, imitating inclusion of metals into tooth tissue. A comparison between FBP, Soft inequalities and barrier function methods is present. Results show that although methods fail to fully remove the artifacts, they succeed in providing some useful features to reconstructed images.

Keywords:

computed tomography, algebraic method, barrier function method, high-absorbing inclusions, metal artifacts.

PP. 117-123.

DOI: 10.14357/20790279180513

References

1. Barrett, J.F., N. Keat. 2004. Artifacts in CT: recognition and avoidance. Radiographics, V 24, № 6. pp. 1679-1691
2. Boas, F.E., Fleischmann, D. 2012 CT artifacts: causes and reduction techniques. Imaging in Medicine. V. 4, № 2. pp. 229240.
3. J.Y. Huang et al. 2015. An evaluation of three commercially available metal artifact reduction methods for CT imaging. Physics in Medicine & Biology. V. 60, № 3. p. 1047.
4. F. Bamberg [и др.]. 2011. Metal artifact reduction by dual energy computed tomography using monoenergetic extrapolation. European radiology. V. 21, № 7. pp. 1424-1429
5. Buls N, et al. 2015 Contrast agent and radiation dose reduction in abdominal CT by a combination of low tube voltage and advanced image reconstruction algorithms. European radiology. V. 2, pp.1023-1031
6. Y. Zhang et al. 2007. Reducing metal artifacts in cone-beam CT images by preprocessing projection data. International Journal of Radiation Oncology Biology Physics. V. 67, № 3. pp. 924-932
7. Nasirudin R.A. et al. 2015. Reduction of metal artifact in single photon-counting computed tomography by spectral-driven iterative reconstruction technique. PloS one. V. 10, №. 5.
8. Park H.S. et al. 2017. Sinogram-consistency learning in CT for metal artifact reduction. arXiv preprint arXiv:1708.00607.
9. Zhang X., Wang J., Xing L. Metal artifact reduction in x‐ray computed tomography (CT) by constrained optimization //Medical physics. – 2011. – Т. 38. – №. 2. – С. 701-711.
10. Sidky E.Y., Pan X. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization //Physics in Medicine & Biology. – 2008. – Т. 53. – №. 17. – С. 4777.
11. Meyer E. et al. Normalized metal artifact reduction (NMAR) in computed tomography //Medical physics. – 2010. – Т. 37. – №. 10. – С. 5482-5493.
12. Oehler M., Buzug T.M. Statistical image reconstruction for inconsistent CT projection data // Methods of information in medicine. – 2007. – Т. 46. – №. 03. – С. 261-269.
13. Chukalina M.V. et al. A Way To Reduce The Artifacts Caused By Intensely Absorbing Areas In Computed Tomography //ECMS. – 2015. – С. 527-531
14. Chukalina M. et al. CT metal artifact reduction by soft inequality constraints //Eighth International Conference on Machine Vision (ICMV 2015). – International Society for Optics and Photonics, 2015. – Т. 9875. – С. 98751C
15. Jorgensen, J.H., Sidky E.Y., Pan X. Analysis of discrete-to-discrete imaging models for iterative tomographic image reconstruction and compressive sensing // IEEE Trans. Med. Imag. – 2011. – Т. 32. – №. 2. – С. 460-473.
16. Brunetti A. et al. A library for X-ray–matter interaction cross sections for X-ray fluorescence applications //Spectrochimica Acta Part B: Atomic Spectroscopy. – 2004. – Т. 59. – №. 10-11. – С. 1725-1731.
17. van Aarle W. et al. The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography //Ultramicroscopy. – 2015. – Т. 157. – С. 35-47
 

 

2024-74-1
2023-73-4
2023-73-3
2023-73-2

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".