Skip to main content
SpringerLink
Log in

Automatic measurement and visualization of focal femoral cartilage thickness in stress-based regions of interest using three-dimensional knee models

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Thinning of cartilage is a common manifestation of osteoarthritis. This study addresses the need of measuring the focal femoral cartilage thickness at the weight- bearing regions of the knee by developing a reproducible and automatic method from MR images.

Methods

3D models derived from semiautomatic MR image segmentations were used in this study. Two different methods were examined for identifying the mechanical loading of the knee articulation. The first was based on a generic weight-bearing regions definition, derived from gait characteristics and cadaver studies. The second used a physically based simulation to identify the patient-specific stress distribution of the femoral cartilage, taking into account the forces and movements of the knee. For this purpose, four different scenarios were defined in our 3D finite element (FE) simulations. The radial method was used to calculate the cartilage thickness in stress-based regions of interest, and a study was performed to validate the accuracy and suitability of the radial thickness measurements.

Results

Detailed focal maps using our simulation data and regional measurements of cartilage thickness are given. We present the outcome of the different simulation scenarios and discuss how the internal/external rotations of the knee alter the overall stress distribution and affect the shape and size of the calculated weight-bearing areas. The use of FE simulations allows for a patient-specific calculation of the focal cartilage thickness.

Conclusion

It is important to assess the quantification of focal knee cartilage morphology to monitor the progression of joint diseases or related treatments. When this assessment is based on MR images, accurate and robust tools are required. In this paper, we presented a set of techniques and methodologies in order to accomplish this goal and move toward personalized medicine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. European Musculoskeletal Conditions Surveillance and Information Network (2012) Musculoskeletal health in Europe: Report v5.0. http://www.eumusc.net Accessed 17 December 2014

  2. Eckstein F, Maschek S, Wirth W, Hudelmaier M, Hitzl W, Wyman B, Nevitt M, Hellio Le Graverand MP (2008) One year change of knee cartilage morphology in the first release of participants from the Osteoarthritis Initiative Progression Subcohort: association with sex, body mass index, symptoms, and radiographic OA status. Ann Rheum Dis 68:674–679

    Article  PubMed  PubMed Central  Google Scholar 

  3. Eckstein F, Cicuttini F, Raynauld J, Waterton JC, Peterfy C (2006) Magnetic resonance imaging (MRI) of articular cartilage in knee osteoarthritis (OA): morphological assessment. Osteoarthr Cartil 14(Suppl A):46–75

    Article  Google Scholar 

  4. Raynauld JP, Kauffmann C, Beaudoin G, Berthiaume MJ et al (2003) Reliability of a quantification imaging system using magnetic resonance images to measure cartilage thickness and volume in human normal and osteoarthritic knees. Osteoarthr Cartil 11:351–360

    Article  PubMed  Google Scholar 

  5. Glaser C, Draeger M, Eckstein F, Englmeier KH, Reiser M (2002) Cartilage loss over two years in femoro-tibial osteoarthritis. Radiology 225(Suppl):330

    Google Scholar 

  6. Koo S, Gold GE, Andriacchi TP (2005) Considerations in measuring cartilage thickness using MRI: factors influencing reproducibility and accuracy. Osteoarthr Cartil 13:782–789

    Article  CAS  PubMed  Google Scholar 

  7. Amin S, LaValley MP, Guermazi A, Grigoryan M, Hunter DJ, Clancy M, Niu J, Gale DR, Felson DT (2005) The relationship between cartilage loss on magnetic resonance imaging and radiographic progression in men and women with knee osteoarthritis. Arthritis Rheum 52:3152–3159

    Article  PubMed  Google Scholar 

  8. Raynauld JP, Martel-Pelletier J, Berthiaume MJ, Beaudoin G et al (2006) Long term evaluation of disease progression through the quantitative magnetic resonance imaging of symptomatic knee osteoarthritis patients: correlation with clinical symptoms and radiographic changes. Arthritis Res Ther 8:R21

    Article  PubMed  PubMed Central  Google Scholar 

  9. Pelletier JP, Raynauld JP, Berthiaume MJ, Abram F, Choquette D et al (2007) Risk factors associated with the loss of cartilage volume on weight-bearing areas in knee osteoarthritis patients assessed by quantitative magnetic resonance imaging: a longitudinal study. Arthritis Res Ther 9:R74

    Article  PubMed  PubMed Central  Google Scholar 

  10. Williams TG, Holmes A, Waterton J, Maciewicz R, Nash A, Taylor C (2006) Regional quantitative analysis of knee cartilage in a population study using MRI and model based correspondences. In: IEEE international symposium on biomedical imaging, Arlington, VA

  11. Williams TG, Holmes AP, Bowes M, Vincent G, Hutchinson CE et al (2010) Measurement and visualisation of focal cartilage thickness change by MRI in a study of knee osteoarthritis using a novel image analysis tool. Br J Radiol 83:940–948

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wirth W, Le Graverand M-PH, Wyman BT, Maschek S, Hudelmaier M et al (2009) Regional analysis of femorotibial cartilage loss in a subsample from the osteoarthritis initiative progression subcohort. Osteoarthr Cartil Osteoarthr Res Soc 17(3):291–297

    Article  CAS  Google Scholar 

  13. Tamez-Peña JG, Barbu-McInnis M, Totterman S (2006) Unsupervised definition of the tibia-femoral joint regions of the human knee and its applications to cartilage analysis. In: Proceedings of SPIE 6144, medical imaging 2006: image processing, 61444K

  14. Duryea J, Iranpour-Boroujeni T, Collins JE et al (2014) Local-area cartilage segmentation (LACS): a semi-automated novel method of measuring cartilage loss in knee osteoarthritis. Arthritis Care Res (Hoboken) 66(10):1560–1565

    Article  Google Scholar 

  15. Bae JY, Park KS, Seon JK et al (2012) Biomechanical analysis of the effects of medial meniscectomy on degenerative osteoarthritis. Med Biol Eng Comput 50(1):53–60

    Article  PubMed  Google Scholar 

  16. Mononen ME, Julkunen P, Töyräs J et al (2011) Alterations in structure and properties of collagen network of osteoarthritic and repaired cartilage modify knee joint stresses. Biomech Model Mechanobiol 10(3):357–369

    Article  CAS  PubMed  Google Scholar 

  17. Pena E, Calvo B, Martinez MA, Doblare M (2006) A three-dimensional finite element analysis of the combined behavior of ligaments and menisci in the healthy human knee joint. J Biomech 39(9):1686–1701

    Article  CAS  PubMed  Google Scholar 

  18. Dong Y, Hu G, Dong Y, Hu Y, Xu Q (2012) The effect of meniscal tears and resultant partial meniscectomies on the knee contact stresses: a finite element analysis. Computer methods in biomechanics and biomedical engineering, pp 1-12

  19. RheumaSCORE (2014) http://www.research.softeco.it/rheumascore.aspx Accessed 17 December 2014

  20. Parascandolo P, Cesario L, Vosilla L, Viano G (2014) Computer aided diagnosis: state-of-the-art and application to musculoskeletal diseases. In: Magnenat-Thalmann N, Ratib O, Choi HF (ed) 3D multiscale physiological human. Springer, pp 277–296. http://www.springer.com/us/book/9781447162742

  21. Softeco Sismat Srl (2014) http://www.softeco.it. Accessed 17 Dec 2014

  22. Barbieri F, Parascandolo P, Vosilla L, Cesario L, Viano G, Cimmino MA (2012) Assessing MRI erosions in the rheumatoid wrist: a comparison between RAMRIS and a semi-automated segmentation software. Ann Rheum Dis 71((Suppl3)):709

    Article  Google Scholar 

  23. Catalano CE, Robbiano F, Parascandolo P, Cesario L, Vosilla L, Barbieri F, Spagnuolo M, Viano G, Cimmino MA (2013) Exploiting 3D part-based analysis, description and indexing to support medical applications. Med Content Based Retr Clin Decis Support LNCS 7723:21–32

    Article  Google Scholar 

  24. Parascandolo P, Cesario L, Vosilla L, Pitikakis M and Viano G (2013) Smart Brush: A real time segmentation tool for 3D medical images. In: IEEE, Image and signal processing and analysis (ISPA), 2013 8th international symposium, pp 689–694

  25. Fedkiw R, Osher S (2002) Level set methods and dynamic implicit surfaces. Springer, Berlin

    Google Scholar 

  26. Bhaidasna Z, Mehta S (2013) A review on level set method for image segmentation. Int J Comput Appl 63(11):20–22

    Google Scholar 

  27. Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3D surface construction algorithm. In: Proceedings of ACM SIGGRAPH, pp 163–169

  28. Kauffmann C, Gravel P, Godbout B, Gravel A et al (2003) Computer-aided method for quantification of cartilage thickness and volume changes using MRI: validation study using a synthetic model. IEEE Trans Biomed Eng 50(8):978–988

    Article  PubMed  Google Scholar 

  29. Chernov N, Ma H (2011) Least squares fitting of quadratic curves and surfaces. In: Yoshida SR (ed) Computer Vision. Nova Science Publishers, pp 285–302. https://www.novapublishers.com/catalog/product_info.php?

  30. Lukács G, Martin R, Marshall D (1998) Faithful least-squares fitting of spheres, cylinders, cones and tori for reliable segmentation. ECCV 1998:671–686

    Google Scholar 

  31. Andrews J, Sequin CH (2013) Type-constrained direct fitting of quadric surfaces. Comput Aided Des Appl 11(1):107–119

    Google Scholar 

  32. Anderson E, Bai Z, Bischof C, Blackford S et al (1999) LAPACK users’ guide. Society for Industrial and Applied Mathematics. doi:10.1137/1.9780898719604

  33. Taubin G (1991) Estimation of planar curves, surfaces and nonplanar space curves defined by implicit equations, with applications to edge and range image segmentation. IEEE Trans Pattern Anal Mach Intell 13:1115–1138. doi:10.1109/34.103273

    Article  Google Scholar 

  34. Alliez P, Rineau L, Tayeb S, Tournois J, Yvinec M (2014) 3D mesh generation. In: CGAL user and reference manual. CGAL Editorial Board, 4.5 edn https://www.cgal.org Accessed 17 Dec 2014

  35. Sibole S, et al (2010) Open knee: a 3D finite element representation of the knee joint. In: 34th annual meeting of the American Society of Biomechanics

  36. Maas SA, Ellis BJ, Ateshian GA et al (2012) FEBio: finite elements for biomechanics. J Biomech Eng 134(1):011005

    Article  PubMed  Google Scholar 

  37. Hemmerich A, Brown H, Smith S et al (2006) Hip, knee, and ankle kinematics of high range of motion activities of daily living. J Orthop Res 24(4):770–781

    Article  CAS  PubMed  Google Scholar 

  38. Kutzner I, Heinlein B, Graichen F et al (2010) Loading of the knee joint during activities of daily living measured in vivo in five subjects. J Biomech 43(11):2164–2173

    Article  CAS  PubMed  Google Scholar 

  39. Wang D, Shi L, Heng PA (2007) Radial thickness calculation and visualization for volumetric layers. In: The Insight Journal—2007 MICCAI open science workshop. http://hdl.handle.net/1926/552. Accessed 17 Dec 2014

  40. Moller T, Trumbore B (1997) Fast, minimum storage ray/triangle intersection. J Graphics Tools 2(1):21–28

    Article  Google Scholar 

  41. Shepherd DE, Seedhom BB (1999) Thickness of human articular cartilage in joints of the lower limb. Ann Rheum Dis 58(1):27–34

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Oshkour AA, Osman NAAbu, Davoodi MM et al (2011) Knee joint stress analysis in standing. In: 5th Kuala Lumpur international conference on biomedical engineering. IFMBE Proceedings 35:179–181

  43. Eckstein F, Ateshian G, Burgkart R et al (2006) Proposal for a nomenclature for magnetic resonance imaging based measures of articular cartilage in osteoarthritis. Osteoarthr Cartil 14(10):974–983

    Article  CAS  PubMed  Google Scholar 

  44. Cotofana S, Buck R, Wirth W, Roemer F, Duryea J, Nevitt M, Eckstein F (2010) Cartilage thickening in early radiographic knee osteoarthritis: a within-person, between-knee comparison. Arthritis Care Res (Hoboken) 64(11):1681–90

    Article  Google Scholar 

  45. The Osteoarthritis Initiative (2014) www.oai.ucsf.edu. Accessed 28 May 2015

  46. Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31:198–211

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was supported by the FP7 Marie Curie Initial Training Network “MultiScaleHuman: Multi-scale Biological Modalities for Physiological Human Articulation”, contract number MRTN-CT-2011-289897. The authors would like to thank the University Hospital of Geneva for the collaboration.

Author information

Authors and Affiliations

  1. Sosteco Sismat S.r.l., Genoa, Italy

    Marios Pitikakis, Lorenzo Cesario, Patrizia Parascandolo, Loris Vosilla & Gianni Viano

  2. MIRALab, University of Geneva, Geneva, Switzerland

    Andra Chincisan & Nadia Magnenat-Thalmann

Authors
  1. Marios Pitikakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andra Chincisan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nadia Magnenat-Thalmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lorenzo Cesario
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Patrizia Parascandolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Loris Vosilla
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Gianni Viano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marios Pitikakis.

Ethics declarations

Conflicts of interest

Marios Pitikakis, Andra Chincisan, Nadia Magnenat-Thalmann, Lorenzo Cesario, Patrizia Parascandolo, Loris Vosilla and Gianni Viano declare that they have no conflict of interest related to the study described in the article.

Statement of informed consent

Informed consent was obtained from all volunteer subjects for being included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pitikakis, M., Chincisan, A., Magnenat-Thalmann, N. et al. Automatic measurement and visualization of focal femoral cartilage thickness in stress-based regions of interest using three-dimensional knee models. Int J CARS 11, 721–732 (2016). https://doi.org/10.1007/s11548-015-1257-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-015-1257-3

Keywords

Search

Navigation