Izvestiya of Saratov University.

Mathematics. Mechanics. Informatics

ISSN 1816-9791 (Print)
ISSN 2541-9005 (Online)


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Bessonov L. V., Golyadkina A. A., Dmitriev P. O., Dol A. V., Zolotov V. S., Ivanov D. V., Kirillova I. V., Kossovich L. Y., Titova Y. I., Ulyanov V. Y., Kharlamov A. V. Constructing the dependence between the Young’s modulus value and the Hounsfield units of spongy tissue of human femoral heads. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2021, vol. 21, iss. 2, pp. 182-193. DOI: 10.18500/1816-9791-2021-21-2-182-193

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
31.05.2021
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English
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Article
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539.3/617.547

Constructing the dependence between the Young’s modulus value and the Hounsfield units of spongy tissue of human femoral heads

Autors: 
Bessonov Leonid Valentinovich, Saratov State University
Golyadkina Anastasiya A., Saratov State University
Dmitriev Pavel O., Saratov State University
Dol Aleksandr Viktorovich, Saratov State University
Zolotov Vladislav S., Saratov State University
Ivanov Dmitriy V., Educational-Research Institute of Nanostructures and Biosystems of Saratov State University
Kirillova Irina Vasil'evna, Saratov State University
Kossovich Leonid Yurevich, Saratov State University
Titova Yuliya I., State Medical University name after V. I. Razumovsky
Ulyanov Vladimir Yu., State Medical University name after V. I. Razumovsky
Kharlamov Aleksandr Vladimirovich, Saratov State University
Abstract: 

Patient-specific biomechanical modeling requires not only the geometric model of the studied object of a particular patient, but also the mechanical properties of its tissues. Quantitative computed tomography provides the initial data for geometric modeling, as well as data on X-ray density (Hounsfield units) of the object. It is known that Hounsfield units correlate with mineral density of the scanned objects, as well as with their strength properties. The aim of this study was to determine the relationship between Hounsfield units and Young's modulus values of human femoral heads spongy tissue. This study was conducted on samples of femur bones spongy tissue. The tissue was obtained from patients who underwent total hip replacement for coxarthrosis. Samples were scanned on a Toshiba Aquilion 64 computed tomograph and then subjected to uniaxial compression on an Instron 5944 universal testing machine. As a result of the study, the average Hounsfield units were obtained for each sample, as well as the Young's modules values. Regression dependencies were calculated linking the Hounsfield units and the Young's modulus values of samples of femoral heads spongy tissue in different types of diseases. The obtained dependences allow one to determine Young's modulus value of femoral heads spongy bone noninvasively for a particular patient, depending on his disease, and using it in the process of preoperative planning. Also, the obtained dependencies can be used in biomechanical modeling of diseases and injuries of vertebral-pelvic complex of a particular patient treatment and can be implemented in medical decision support system in surgery of vertebral-pelvic complex.

Acknowledgments: 
The work was supported by the Russian Foundation for Advanced Research.
References: 
  1. Patel S. P., Lee J. J., Hecht G. G., Holcombe S. A., Wang S. C., Goulet J. A. Normative vertebral Hounsfield unit values and correlation with bone mineral density. Journal of Clinical & Experimental Orthopaedics, 2016, vol. 2, no. 14, pp. 1–7. https://doi.org/10.4172/2471- 8416.100014
  2. Kim K. J., Kim D. H., Lee J. I., Choi B. K., Han I. H., Nam K. H. Hounsfieldunits on lumbar computed tomography for predicting regional bone mineral density. Open Medicine, 2019, vol. 14, pp. 545–551. https://doi.org/10.1515/med-2019-0061
  3. Khan S. N., Warkhedkar R. M., Shyam A. K. Analysis of Hounsfield unit of human bones for strength evaluation. Procedia Materials Science, 2014. vol. 6, pp. 512–519. https://doi.org/10.1016/j.mspro.2014.07.065
  4. Giambini H., Dragomir-Daescu D., Huddleston P. M., Camp J. J., An K. N., Nassr A. The effect of quantitative computed tomography acquisition protocols on bone mineral density estimation. Journal of Biomechanical Engineering, 2015, vol. 137, no. 11, pp. 114502. https://doi.org/10.1115/1.4031572
  5. Cyganik L., Binkowski M., Kokot G., Rusin T., Popik P., Bolechala F., Nowak R. Wrobel Z., John A. Prediction of Young’s modulus of trabeculae in microscale using macro-scale’s relationships between bone density and mechanical properties. Journal of the Mechanical Behavior of Biomedical Materials, 2014, vol. 36, pp. 120–134. https://doi.org/10.1016/j.jmbbm.2014.04.011
  6. Michalski A. S., Edwards W. B., Boyd S. K. The influence of reconstruction kernel on bone mineral and strength estimates using quantitative computed tomography and finite element analysis. Journal of Clinical Densitometry, 2019, vol. 22, iss. 2, pp. 219–228. https://doi.org/10.1016/j.jocd.2017.09.001
  7. Andersen H. K., Jensen K., Berstad A. E., Aalokken T. M., Kristiansen J., von Gohren Edwin B, Hagen G., Martinsen A. C. Choosing the best reconstruction technique in abdominal computed tomography: A systematic approach. Journal of Computer Assisted Tomography, 2014, vol. 38, iss. 6, pp. 853–858. https://doi.org/10.1097/RCT.0000000000000139
  8. Birnbaum B. A., Hindman N., Lee J., Babb J. S. Multi-detector row CT attenuation measurements: Assessment of intra- and interscanner variability with an anthropomorphic body CT phantom. Radiology, 2007, vol. 242, no. 1, pp. 109–119.
  9. Ivanov D. V., Kirillova I. V., Kossovich L. Yu., Bessonov L. V., Petraikin A. V., Dol A. V., Ahmad E. S., Morozov S. P., Vladzymyrskyy A. V., Sergunova K. A., Kharlamov A. V. Influence of convolution kernel and beam-hardening effection the assessment of trabecular bone mineral density using quantitative computed tomography. Izvestiya of Saratov University. New Series. Series: Mathematics. Mechanics. Informatics, 2020, vol. 20, iss. 2, pp. 205–219. https://doi.org/10.18500/1816-9791-2020-20-2-205-219
  10. Currey J. D. Tensile yield in compact bone is determined by strain, post-yield behaviour by mineral content. Journal of Biomechanics, 2004, vol. 37, iss. 4, pp. 549–556. https://doi.org/10.1016/j.jbiomech.2003.08.008
  11. Chen W.-P., Hsu J.-T., Chang C.-H. Determination of young’s modulus of cortical bone directly from computed tomography: A rabbit model. Journal of the Chinese Institute of Engineers, 2003, vol. 26, no. 6, pp. 737–745. https://doi.org/10.1080/02533839.2003.9670828
  12. Giambini H., Dragomir-Daescu D., Nassr A., Yaszemski M. J., Zhao C. Quantitative computed tomography protocols affect material mapping and quantitative computed tomography-based finite-element analysis predicted stiffness. Journal of Biomechanical Engineering, 2016, vol. 138, iss. 9, pp. 091003-1–091003-7. https://doi.org/10.1115/1.4034172
  13. Helgason B., Perilli E., Schileo E., Taddei F., Brynjolfsson S., Viceconti M. Mathematical relationships between bone density and mechanical properties: A literature review. Clinical Biomechanics, 2008, vol. 23, no. 2, pp. 135–146. https://doi.org/10.1016/j.clinbiomech.2007.08.024
  14. Witt R. M., Cameron J. R. Improved Bone Standard Containing Dipotassium Hydrogen Phosphate Solution for The Intercomparison of Different Transmission Bone Scanning Systems. United States: N. p., 1971. 6 p. https://doi.org/10.2172/4054820
  15. Omelchenko T. M., Buryanov O. A., Lyabakh A. P., Mazevich V. B., Shidlovsky M. S., Musienko O. S. Correlation of elastic modulus and x-ray bone density in the area of the ankle joint. Orthopedics, Traumatology and Prosthetics, 2018, no. 3, pp. 80–84 (in Ukraine). http://dx.doi.org/10.15674/0030-59872018380-84
  16. Dmitriev P. O., Golyadkina A. A., Bessonov L. V., Kirillova I. V., Kossovich L. Yu., Falkovich A. S. The dependence of Young’s modulus of trabecular bony tissue on its density according to computed tomography. Progress in Biomedical Optics and Imaging — Proceedings of SPIE, 2019, vol. 11229, article no. 112291L. https://doi.org/10.1117/12.2545077
  17. Petraikin A. V., Ivanov D. V., Akhmad E. S., Sergunova K. A., Nizovtsova L. A., Petryaykin F. A., Ruzov S. A., Kirilova I. V., Kossovich L. Yu., Bessonov L. V., Dol A. V., Vladzymyrskyy A. V., Harlamov A. V. Phantom modeling for selection of optimum reconstruction filters in the quantitative computer tomography. Meditsinskaya Fizika [Medical Physics], 2020, vol. 86, no. 2, pp. 34–44 (in Russian).
  18. Glanc S. Mediko-biologicheskaya statistika [Medical and Biological Statistics]. Moscow, Praktika, 1998. 459 р. (in Russian).
  19. Kobzar’ A. I. Prikladnaya matematicheskaya statistika: Dlya inzhenerov i nauchnyh rabotnikov [Applied Mathematical Statistics: For Engineers and Scientists]. Moscow, Fizmatlit, 2006. 816 p. (in Russian).
  20. Free J., Eggermont F., Derikx L., van Leeuwen R., van der Linden Y., Jansen W., Raaijmakers E., Tanck E., Kaatee R. The effect of different CT scanners, scan parameters and scanning setup on Hounsfield units and calibrated bone density: A phantom study. Biomedical Physics & Engineering Express, 2018, vol. 4, no. 5, pp. 12. https://doi.org/10.1088/2057-1976/aad66a
Received: 
21.09.2020
Accepted: 
03.11.2020
Published: 
31.05.2021