VALIDATION OF A THREE-DIMENSIONAL HEAD PHANTOM FOR IMAGING DATA

Jolanta Podolszańska

doi:10.35784/iapgos.3663

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Published: Sep 30, 2023

DOI: https://doi.org/10.35784/iapgos.3663

DOI

https://doi.org/10.35784/iapgos.3663

Authors

Jolanta Podolszańska

jolanta.podolszanska@outlook.com

Częstochowa University of Technology

https://orcid.org/0000-0002-6032-5654

Abstract

This paper presents the research results on the design of a three-dimensional head phantom for cone beam projection. The head model is based on a Shepp-Logan mathematical head model, which is used to simulate the operation of the CT scanner. The model is then compared with the reference data for structural similarity, reasoning, and shape. The geometric parameters of the obtained images are investigated. The reconstructed image is analyzed using the FDK method. The results show that the geometric parameters directly correlate with the number of projections. A mathematical framework of cone beam 3d reconstruction via the first derivative of the radon transform is presented.

Keywords:

computed tomography, FDK reconstruction, 3D mathematics phantom model, Shepp-Logan phantom

References

Batur A. et al.: Hounsfield unit density in the characterization of bile duct lesions. Polish Journal of Radiology 84, 2019, 397–401. DOI: https://doi.org/10.5114/pjr.2019.89390

Dzierżak R., et al.: The influence of the normalization of spinal CT images on the significance of textural features in identifying defects in the spongy tissue structure. Innovations in Biomedical Engineering. Springer International Publishing, 2019. DOI: https://doi.org/10.1007/978-3-030-15472-1_7

Hansen P. C. et al.: Computed tomography: algorithms, insight, and just enough theory. Society for Industrial and Applied Mathematics, 2021. DOI: https://doi.org/10.1137/1.9781611976670

Ilmavirta J., Monkkonen K.: X-ray tomography of one-forms with partial data. SIAM Journal on Mathematical Analysis 53(3), 2021, 3002–3015. DOI: https://doi.org/10.1137/20M1344779

Panetta D., Camarlinghi N.: 3D Image Reconstruction for CT and PET: A Practical Guide with Python. CRC Press 2020, 65–73. DOI: https://doi.org/10.1201/9780429270239

Senchukova A.: Learned image reconstruction in X-ray computed tomography, 2020.

Sun W. et al.: Review of high energy x-ray computed tomography for non-destructive dimensional metrology of large metallic advanced manufactured components. Reports on Progress in Physics 85(1), 2022, 016102. DOI: https://doi.org/10.1088/1361-6633/ac43f6

Tadeusiewicz R.: Komputerowe systemy wizyjne w zastosowaniach przemysłowych. Utrzymanie Ruchu 3, 2019, 14–21.

Withers P. J. et al.: X-ray computed tomography. Nature Reviews Methods Primers 1(1), 2021, 18. DOI: https://doi.org/10.1038/s43586-021-00015-4

Xu X. et al.: Review of electromagnetic vibration in electrical machines. Energies 11(7), 2018, 1779. DOI: https://doi.org/10.3390/en11071779

Zuo C. et al.: Transport of intensity equation: a tutorial. Optics and Lasers in Engineering 135, 2020, 106187. DOI: https://doi.org/10.1016/j.optlaseng.2020.106187

Podolszańska, J. (2023). VALIDATION OF A THREE-DIMENSIONAL HEAD PHANTOM FOR IMAGING DATA. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(3), 29–32. https://doi.org/10.35784/iapgos.3663

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