Advertisement

Medical & Biological Engineering & Computing

, Volume 57, Issue 10, pp 2305–2318 | Cite as

Measurement of range of motions of L3-L4 healthy spine through offsetting reflective markers and in silico analysis of meshed model

  • G. Kosalishkwaran
  • S. ParasuramanEmail author
  • D. Kingsly Jeba Singh
  • Elango Natarajan
  • I. Elamvazuthi
  • John George
Original Article
  • 142 Downloads

Abstract

Degenerative disc disease (DDD) is a common condition in elderly population that can be painful and can significantly affect individual’s quality of life. Diagnosis of DDD allows prompt corrective actions but it is challenging due to the absence of any symptoms at early stages. In studying disc degeneration, measurement of the range of motion (RoM) and loads acting on the spine are crucial factors. However, direct measurement of RoM involves increased instrumentation and risk. In this paper, an innovative method is proposed for calculating RoM, emphasizing repeatability and reliability by considering the posterior thickness of the spine. This is achieved by offsetting the position of markers in relation to the actual vertebral loci. Three geometrically identical finite element models of L3-L4 are developed from a CT scan with different types of elements, and thereafter, mesh element-related metrics are provided for the assessment of the quality of models. The model with the best mesh quality is used for further analysis, where RoM are within ranges as reported in literature and in vivo experiment results. Various kinds of stresses acting on individual components including facet joints are analysed for normal and abnormal loading conditions. The results showed that the stresses in abnormal load conditions for all components including cortical (76.67 MPa), cancellous (69.18 MPa), annulus (6.30 MPa) and nucleus (0.343 MPa) are significantly greater as compared to normal loads (49.96 MPa, 44.2 MPa, 4.28 MPa and 0.23 MPa respectively). However, stress levels for both conditions are within safe limits (167–215 MPa for cortical, 46 MPa for the annulus and 3 MPa for facets). The results obtained could be used as a baseline motion and stresses of healthy subjects based on their respective lifestyles, which could benefit clinicians to suggest corrective actions for those affected by DDD.

Keywords

Biomechanics Kinesis Kinematics Lumbar spine Biomedical image processing In silico 

Notes

Funding information

This work was supported by the Fundamental Research Grant Scheme (FRGS), JABATAN PENGAJIAN TINGGI KEMENTERIAN PENDIDIKAN MALAYSIA under Grant Project No.: FRGS/ 1/ 2015 / TK03 / MUSM / 02 / 1 and the DST – FIST (Government of India) under Grant Ref. No. SR / FST/ ETI – 311 / 2012.

Compliance with ethical standards

Ethical approval

Ethical approval for the current research study was obtained from the Monash University Human Research Ethics Committee (MUHREC), Australia, and all participants provided with written consent forms. Reference: CF16/2165 - 2016001072, Name of the Ethic Committee: Monash University Human Research Ethics Committee (MUHREC), Australia.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

  1. 1.
    Wang H, Naghavi M, Allen C, Barber R, Bhutta Z, Carter A, Casey D, Charlson F, Chen A, Coates M et al (2016) Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388:1603–1658.  https://doi.org/10.1016/S0140-6736(16)31460-X CrossRefGoogle Scholar
  2. 2.
    Adams MA (2004) Biomechanics of back pain. Acupunct Med 22:178–188.  https://doi.org/10.1136/aim.22.4.178 CrossRefPubMedGoogle Scholar
  3. 3.
    Cramer GD (2014) The lumbar region. In: Darby SA, Cramer GD, Huff TG, Cummings SA, Mensching R, Sistak K, Louis ES (eds) Clinical anatomy of the spine, spinal cord, and ANS, 3rd edn. Elsevier, St. Louis, pp 246–248CrossRefGoogle Scholar
  4. 4.
    Wang S, Xia Q, Passias P, Li W, Wood K, Li G (2011) How does lumbar degenerative disc disease affect the disc deformation at the cephalic levels in vivo? Spine 36:E574–E581.  https://doi.org/10.1097/BRS.0b013e3181f79e93 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    White AA, Panjabi MM (1978) The basic kinematics of the human spine: a review of past and current knowledge. Spine 3:12–20.  https://doi.org/10.1097/00007632-197803000-00003 CrossRefPubMedGoogle Scholar
  6. 6.
    Cook DJ, Yeager MS, Cheng BC (2015) Range of motion of the intact lumbar segment: a multivariate study of 42 lumbar spines. Int J Spine Surg 9:5.  https://doi.org/10.14444/2005 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Pearcy JM, Tibrewal BS (1984) Axial rotation and lateral bending in the normal lumbar spine measured by three-dimensional radiography. Spine 9:582–587.  https://doi.org/10.1097/00007632-198409000-00008 CrossRefPubMedGoogle Scholar
  8. 8.
    Pearcy M, Portek I, Shepherd J (1984) Three-dimensional X-ray analysis of normal movement in the lumbar spine. Spine 9:294–297.  https://doi.org/10.1097/00007632-198404000-00013 CrossRefPubMedGoogle Scholar
  9. 9.
    Zafereo J, Wang-Price S, Brown J, Carson E (2016) Reliability and comparison of spinal end-range motion assessment using a skin-surface device in participants with and without low back pain. J Manip Physiol Ther 39:434–442.  https://doi.org/10.1016/j.jmpt.2016.05.008 CrossRefGoogle Scholar
  10. 10.
    Chen C-H, Chen-Sheng C, Chang-Jung C (2017) Assessment of the suitability of biodegradable rods for use in posterior lumbar fusion: an in-vitro biomechanical evaluation and finite element analysis. PLoS One 12:e0188034.  https://doi.org/10.1371/journal.pone.0188034 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Yun-Peng H, Cheng-Fei D, Cheng-Kung C, Zheng-Cheng Z, Xuan-Wei C, Wu G, Zhe-Cheng L, Jian-Hua L, Wang L (2016) Preserving posterior complex can prevent adjacent segment disease following posterior lumbar interbody fusion surgeries: a finite element analysis. PLoS One 11:e0166452.  https://doi.org/10.1371/journal.pone.0166452 CrossRefGoogle Scholar
  12. 12.
    Dreischarf M, Zander T, Shirazi-Adl A, Puttlitz CM, Adam CJ, Chen CS, Goel VK, Kiapour A, Kim YH, Labus KM, Little JP, Park WM, Wang YH, Wilke HJ, Rohlmann A, Schmidt H (2014) Comparison of eight published static finite element models of the intact lumbar spine: predictive power of models improves when combined together. J Biomech 47:1757–1766.  https://doi.org/10.1016/j.jbiomech.2014.04.002 CrossRefPubMedGoogle Scholar
  13. 13.
    Little J, Adam CJ (2013) Geometric sensitivity of patient-specific finite element models of the spine to variability in user-selected anatomical landmarks. 18Google Scholar
  14. 14.
    Sevrain A, Aubin C-E, Gharbi H, Wang X, Labelle H (2012) Biomechanical evaluation of predictive parameters of progression in adolescent isthmic spondylolisthesis: a computer modeling and simulation study. Scoliosis 7(2).  https://doi.org/10.1186/1748-7161-7-2
  15. 15.
    Huang JY, Li HY, Jian FZ, Yan HG (2012) The finite element modeling and analysis of human lumbar segment herniation. 12:394–398Google Scholar
  16. 16.
    Wang W, Zhang H, Sadeghipour K, Baran G (2013) Effect of posterolateral disc replacement on kinematics and stress distribution in the lumbar spine: a finite element study. (clinical report). Med Eng Phys 35:357–364.  https://doi.org/10.1016/j.medengphy.2012.05.013 CrossRefPubMedGoogle Scholar
  17. 17.
    Zapata-Cornelio F, Day G, Coe R, Sikora S, Wijayathunga V, Tarsuslugil S, Mengoni M, Wilcox R (2017) Methodology to produce specimen-specific models of vertebrae: application to different species. J Biomed Eng Soc 45:2451–2460.  https://doi.org/10.1007/s10439-017-1883-8 CrossRefGoogle Scholar
  18. 18.
    Xu M, Yang J, Lieberman IH, Haddas R (2017) Lumbar spine finite element model for healthy subjects: development and validation. Comput Methods Biomech Biomed Eng 20:1–15.  https://doi.org/10.1080/10255842.2016.1193596 CrossRefGoogle Scholar
  19. 19.
    Burkhart TA, Andrews DM, Dunning CE (2013) Finite element modeling mesh quality, energy balance and validation methods: a review with recommendations associated with the modeling of bone tissue. J Biomech 46:1477–1488.  https://doi.org/10.1016/j.jbiomech.2013.03.022 CrossRefPubMedGoogle Scholar
  20. 20.
    Chakraverty R, Pynsent P, Isaacs K (2007) Which spinal levels are identified by palpation of the iliac crests and the posterior superior iliac spines? J Anat 210:232–236.  https://doi.org/10.1111/j.1469-7580.2006.00686.x CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Bai X, Liu G, Xu C, Zhuang Y, Zhang J, Jia Y, Liu Y (2012) Morphometry research of deer, sheep, and human lumbar spine: feasibility of using deer and sheep in spinal animal models. Int J Morphol 30:510–520CrossRefGoogle Scholar
  22. 22.
    Nayate AP, Nasrallah IM, Schmitt JE, Mohan S (2016) Using body mass index to predict needle length in fluoroscopy-guided lumbar punctures. AJNR Am J Neuroradiol 37:572–578.  https://doi.org/10.3174/ajnr.A4579 CrossRefPubMedGoogle Scholar
  23. 23.
    Wong JS, Suresh P (2018) Imaging the spine. Surgery (Oxford) 36:370–382.  https://doi.org/10.1016/j.mpsur.2018.03.018 CrossRefGoogle Scholar
  24. 24.
    Silva MJ, Wang C, Keaveny TM, Hayes WC (1994) Direct and computed tomography thickness measurements of the human, lumbar vertebral shell and endplate. Bone 15:409–414.  https://doi.org/10.1016/8756-3282(94)90817-6 CrossRefPubMedGoogle Scholar
  25. 25.
    Mooney M (1940) A theory of large elastic deformation. J Appl Phys 11(9):582–591CrossRefGoogle Scholar
  26. 26.
    Shahzad M, Kamran A, Siddiqui MZ, Farhan M (2015) Mechanical characterization and FE modelling of a hyperelastic material. Mater Res 18:918–924CrossRefGoogle Scholar
  27. 27.
    Elango N, Faudzi AAM, Hassan A, Rusydi MRM (2014) Experimental investigations of skin-like material and computation of its material properties. Int J Precis Eng Manuf 15:1909–1914.  https://doi.org/10.1007/s12541-014-0545-0 CrossRefGoogle Scholar
  28. 28.
    Elango N, Srinivasa Gupta N, Lih Jiun Y, Golshahr A (2017) The effect of high loaded multiwall carbon nanotubes in natural rubber and their nonlinear material constants. J Nanomater 2017(6193961):15–15.  https://doi.org/10.1155/2017/6193961 CrossRefGoogle Scholar
  29. 29.
    Elango N, Faudzi AAM (2015) A review article: investigations on soft materials for soft robot manipulations. Int J Adv Manuf Technol 80:1027–1037.  https://doi.org/10.1007/s00170-015-7085-3 CrossRefGoogle Scholar
  30. 30.
    Elango N, Marappan R (2011) Analysis on the fundamental deformation effect of a robot soft finger and its contact width during power grasping. Int J Adv Manuf Technol 52:797–804.  https://doi.org/10.1007/s00170-010-2747-7 CrossRefGoogle Scholar
  31. 31.
    Elango N et al (2014) Determination of non-linear material constants of RTV silicone applied to a soft actuator for robotic applications. Key Eng Mater 594-595:1099–1104CrossRefGoogle Scholar
  32. 32.
    Tsouknidas A, Savvakis S, Asaniotis Y, Anagnostidis K, Lontos A, Michailidis N (2013) The effect of kyphoplasty parameters on the dynamic load transfer within the lumbar spine considering the response of a bio-realistic spine segment. Clin Biomech 28:949–955.  https://doi.org/10.1016/j.clinbiomech.2013.09.013 CrossRefGoogle Scholar
  33. 33.
    Denoziere G, Ku D (2006) Biomechanical comparison between fusion of two vertebrae and implantation of an artificial intervertebral disc. J Biomech 39:766–775.  https://doi.org/10.1016/j.jbiomech.2004.07.039 CrossRefPubMedGoogle Scholar
  34. 34.
    Yamamoto I, Panjabi MM, Crisco T, Oxland T (1989) Three-dimensional movements of the whole lumbar spine and lumbosacral joint. Spine 14:1256–1260.  https://doi.org/10.1097/00007632-198911000-00020 CrossRefPubMedGoogle Scholar
  35. 35.
    Bartlett JW, Frost C (2008) Reliability, repeatability and reproducibility: analysis of measurement errors in continuous variables. Ultrasound Obstet Gynecol 31:466–475.  https://doi.org/10.1002/uog.5256 CrossRefPubMedGoogle Scholar
  36. 36.
    Choi J, Shin D-A, Kim S (2017) Biomechanical effects of the geometry of ball-and-socket artificial disc on lumbar spine: a finite element study. SPINE 42:E332–E339.  https://doi.org/10.1097/BRS.0000000000001789 CrossRefPubMedGoogle Scholar
  37. 37.
    Lin HS, Liu YK, Adams KH (1978) Mechanical response of the lumbar intervertebral joint under physiological (complex) loading. J Bone Joint Surg Am 60:41–55CrossRefGoogle Scholar
  38. 38.
    H. Schmidt, A. Kettler, F. Heuer, U. Simon, L. Claes, and H.-J. Wilke, Intradiscal pressure, shear strain, and fiber strain in the intervertebral disc under combined loading. 2007, pp. 748–55Google Scholar
  39. 39.
    An YH, Draughn RA (2000) Mechanical testing of bone and the bone-implant interface. In: An YH, Draughn RA (eds) . CRC Press, Boca RatonGoogle Scholar
  40. 40.
    Skaggs LD, Weidenbaum CM, Latridis CJ, Ratcliffe CA, Mow CV (1994) Regional variation in tensile properties and biochemical composition of the human lumbar anulus fibrosus. Spine 19:1310–1319.  https://doi.org/10.1097/00007632-199406000-00002 CrossRefPubMedGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  • G. Kosalishkwaran
    • 1
  • S. Parasuraman
    • 1
    Email author
  • D. Kingsly Jeba Singh
    • 2
  • Elango Natarajan
    • 3
  • I. Elamvazuthi
    • 4
  • John George
    • 5
  1. 1.School of EngineeringMonash University MalaysiaBandar SunwayMalaysia
  2. 2.Department of Mechanical EngineeringSRM UniversityKattankulathurIndia
  3. 3.Faculty of EngineeringUCSI UniversityKuala LumpurMalaysia
  4. 4.Department of Electrical & Electronic EngineeringUniversity Teknologi PetronasSeri IskandarMalaysia
  5. 5.Research Imaging CentreUniversity of MalayaKuala LumpurMalaysia

Personalised recommendations