Simulation of a slice phantom of human vertebra trabecular tissue for the study of its CT reconstruction
DOI:
https://doi.org/10.30837/rt.2025.4.223.18Keywords:
bone trabecular structure, synthetic CT phantom, Radon transform, inverse reconstruction, noiseAbstract
The relevance of the study is due to the need to assess the influence of CT reconstruction settings on the accuracy of calculating the parameters of the trabecular bone of the vertebrae. The aim of this study is to develop a digital phantom that accurately reflects the microarchitecture of trabecular bone tissue and can be used to model the results in real scanning conditions, with subsequent comparative analysis of morphometric parameters of cancellous bone obtained using CT. The phantom model of the trabecular bone section was developed based on histological characteristics obtained from micro-CT images. The complete workflow for the synthesis of a digital phantom of bone structures is based on micro-CT with subsequent complex data processing, including sinogram generation and image reconstruction. The projection sinogram was generated using the Radon transform with a predefined angular step, and then subjected to CT image reconstruction. The procedure for constructing and studying a digital CT phantom consisted of several key steps: phantom generation, simulation of its CT image under conditions close to those of real clinical scans, and subsequent quantitative assessment of discrepancies between phantom image parameters and the reconstructed image. The results demonstrate the possibility of modeling trabecular bone architecture using a digital phantom, which provides a reproducible basis for quantitative assessment of bone microstructure. All stages of calculations and modeling were performed in the MATLAB environment (R2023b, MathWorks version) using the Image Processing Toolbox for image processing, as well as specially developed scripts for morphometric analysis adapted to the specific characteristics of trabecular architecture.
References
Molino, Giulia, Giorgia Montalbano, Carlotta Pontremoli, Sonia Fiorilli, and Chiara Vitale-Brovarone. Imaging Techniques for the Assessment of the Bone Osteoporosis-Induced Variations with Particular Focus on Micro-CT Poten-tial // Applied Sciences. 2020. Vol. 10, no. 24. Р. 8939. doi:10.339010248939
Filimonov S., Liliia A. and Tymkovych M. Analysis of CT Parameters of Trabecular Tissue Density of Verte-bral Body // 2024 IEEE 5th KhPI Week on Advanced Technology (KhPIWeek), Kharkiv, Ukraine, 2024. P. 1–4. doi: 10.1109/KhPIWeek61434.2024.10878080.
Brett A.D., Brown J.K. Quantitative computed tomography and opportunistic bone density screening by dual use of computed tomography scans // J Orthop Translat, 2015. doi: 10.26044/ecr2020/C-00759.
Christiansen B.A. Effect of micro-computed tomography voxel size and segmentation method on trabecular bone microstructure measures in mice. Bone Rep, 2016. doi: 10.1016/j.jot.2015.08.006.
Manohar, Srirang, Ioannis Sechopoulos, Mark A. Anastasio, et al. Super phantoms: advanced models for testing medical imaging technologies // Communications Engineering. 2024. Vol. 3. P. 73. doi:10.1038/s44172-024-00218-z
Galassi F.M., Lorkiewicz W., Filipiak J. et al. Age- and sex-related changes in vertebral trabecular bone archi-tecture in Neolithic and Mediaeval populations from Poland // Sci Rep. 2024. Vol. 14. P. 9977. https://doi.org/10.1038/s41598-024-59946-z
Tan X., Liu X., Xiang K., Wang J., Tan S. Deep Filtered Back Projection for CT Reconstruction // IEEE Ac-cess. 2024. Vol. 12. P. 20962–20972. doi:10.1109/ACCESS.2024.3357355.
Hwang G., Kim J., Lee Y. Inversion formulas for quarter-spherical Radon transforms // AIMS Mathematics. 2023. Vol. P. 1600. doi:10.3934/math.20231600.
Beckmann M., Nickel J. Optimized filter functions for filtered back projection reconstructions // Inverse Prob-lems & Imaging. 2025. doi:10.3934/ipi.2025003
Hein D., Karchev P., Freichel S., et al. Noise suppression in photon-counting CT via posterior sampling in Poisson-flow generative models // Visual Computing for Industry, Biomedicine and Art. 2024. Vol. 7. P. 6. doi:10.1186/s42492-024-00175-6.
Koetzier L.R., Mastrodicasa D., Szczykutowicz T.P., et al. Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects // Radiology. 2023. Vol. 306(3). P. e221257. doi:10.1148/radiol.221257.
Oh S., Kang W.Y., Park H., et al. Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening // Scientific Reports. 2024. Vol. 14. P. 363. doi:10.1038/s41598-023-45824-7.
Lukova A., Dunmore C.J., Tsegai Z.J., et al. Technical note: Does scan resolution or downsampling impact the analysis of trabecular bone architecture? // American Journal of Biological Anthropology. 2024. Vol. 185(3). P. e25023. doi:10.1002/ajpa.25023.
Buyuksungur A., Turan B., Atik O., et al. The Effect of Micro-Computed Tomography Thresholding Methods on Bone Micromorphometric Analysis // Biomedicines. 2024. Vol. 12(11). P. 343. doi:10.3390/biomedicines15110343.
Kok J., Bevers M.S.A.M., van Rietbergen B., Oei E.H.G., Booij R. Quantification of bone microarchitecture using photon-counting CT at different radiation doses: A comparison with µCT // European Journal of Radiology. 2024. Vol. 181. P. 111717. doi:10.1016/j.ejrad.2024.111717.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
