Functional Imaging

Functional imaging of bone

Radiographic images of human femora are common tools for diagnosis in clinics. For example, they are used for evaluating the severity of osteoporosis and osteoarthritis. Our aim is to help diagnosis by building new computational methods that can gather more information from these images.

Measurement of 2D bone mineral density (BMD) with dual-energy X-ray absorptiometry (DXA) imaging of proximal femur is the gold standard method for diagnosis of osteoporosis and for predicting fracture risk. However, the DXA images alone are only a moderate predictor of fractures. We believe that with mechanical modeling we can predict the fractures more accurately since then also the geometry of bone is included in prediction of fracture. Therefore, we developed a method to estimate the 3D shape and internal density of the proximal femur based on one DXA image. Then, this estimated shape can be feed for finite element analysis (FEA) to get a subject specific mechanical model and fracture model. From this model we can calculate the maximum strength that the patient's proximal femur can bear without fracture.

The videos present the estimation of the 3D shape and internal density of a proximal femur based on a 2D DXA image. (a) A template which includes general information about femur shape and density distribution is built from femoral CT images. Then, the shape of this template is fitted to the shape of 2D DXA image and the internal density is matched with the one from the DXA image.


Väänänen Sami P., Isaksson Hanna, Julkunen Petro, Sirola Joonas, Kröger Heikki, Jurvelin Jukka S., Assessment of the 3-D shape and mechanics of the proximal femur using a shape template and a bone mineral density image, Biomechanics and modeling in mechanobiology, 4 529—538 (2011)

Väänänen Sami P., Jurvelin Jukka S., and Isaksson Hanna, Estimation of 3-D shape, internal density and mechanics of proximal femur by combining bone mineral density images with shape and density templates, Biomechanics and modeling in mechanobiology 6:791-800 (2012)

A common way to evaluate the geometry of the proximal femur is to take measures from the femoral radiograph, i.e., shape parameters. Then they can be correlated with clinical parameters such as severity of osteoarthritis or osteoporosis to find their relation or to use them as predictor of the clinical parameters. However, each of these shape parameters measure only one local feature of the shape. Therefore, we have built a statistical shape model that can describe the variation in both shape and density of femur in the evaluated population with a few parameters, called modes. The base of the statistical shape model is the principal component analysis. In one application we used the statistical shape model when we created new femur samples from a population. It is possible since the modes describe the variation of the shape and density within the training population (see video). We have also used the statistical shape model for predicting the 3D orientation of the femur in 2D radiographs.

The mode values in the statistical shape model describe the variation of both the shape and internal density in the population’s proximal femora. The value of a mode defines how far the shape is from the average of the population. The unit is standard deviations.


Väänänen Sami P., Isaksson Hanna, Waarsing Erwin, Zadpoor Amir Abbas, Jurvelin Jukka S., Weinans Harrie, Estimation of 3D rotation of femur in 2D hip radiographs, Journal of Biomechanics (2012) accepted for publication.

Functional imaging of knee joint

MRI is commonly used for evaluating condition of knee joint. Since MRI offers relatively good contrast between solid and soft tissues, and furthermore, it offers information about compositions and structures in different joint tissues (cartilage, meniscus), it is used for diagnosing degenerative changes in knee joint, such as cartilage and meniscal injuries [1, 2]. However, MRI does not reveal function of knee joint during different physical activity (walking, running). Therefore, different computational models have been developed estimating varying forces and strains in the knee joint.

In the recent studies [3-5], realistic loading input has been implemented into knee joint models. However, these models have lacks in realistic material characterization for different joint tissues. Cartilages of knee joint have been usually considered as linear isotropic material [3-5], while in reality they have highly anisotropic and time-dependent properties due to depth-dependent collagen fibril network, proteoglycan density and fluid fraction [6-8]. Since all knee joint loading forces are transferred through cartilage tissues to bone, effects of previous compositions should be considered as realistically as possible. This is possible by using fibril-reinforced biphasic properties for cartilage tissues (See Biomechanics and modeling).

For simulating realistically effects of different joint loadings in addition to realistic material characterization, varying knee loadings, rotations and translations (movements of femur respect with to tibia) is needed. This data can be obtained by using different gait analysis methods, such as fluoroscopic setup system or markers [9, 10]. When computational model with realistic material characterizations for different joint tissues and realistic data during physical activity is combined, realistic simulations can be done (11-13). This approach can be used for designing and optimizing treatment operations, such as partial meniscectomy or cartilage/menisucs repair. Furthermore, presented tool can be used for estimating development\probability of osteoarthritis in knee joint.


Comparison of stress variations within knee joint during axial impact loading with different collagen fibril architecture in the tibial cartilage: figures show results of different collagen architectures at the center of medial compartment of the knee joint. In the left figure, collagen orientations are based on the T2-mapped MRI data, whereas in the right figure, collagen orientations are obtained from the literature.

Stress variations in the intact knee joint (top video), in the knee joint with middle radial tear of lateral meniscus (middle video) and in the knee joint with partial lateral meniscectomy during normal walking (bottom video).


  1. Braun, J.J., Gold, G.E., 2012. Diagnosis of osteoarthritis: Imaging. Bone 51:278-88
  2. Roemer, F.W., Crema, M.D., Trattnig, S., Guermazi, A., 2011. Advances in imaging of osteoarthritis and cartilage. Radiology 260:332-354
  3. Netravali NA, Koo S, Giori NJ, Andriacchi TP. 2011. The effect of kinematic and kinetic changes on meniscal strains during gait. J Biomech Eng 133:011006.
  4. Yang NH, Nayeb-Hashemi H, Canavan PK, Vaziri A. 2010. Effect of frontal plane tibiofemoral angle on the stress and strain at the knee cartilage during the stance phase of gait. J Orthop Res 28:1539-1547.
  5. Saveh AH, Katouzian HR, Chizari M. 2011. Measurement of an intact knee kinematics using gait and fluoroscopic analysis. Knee Surg Sports Traumatol Arthrosc 19:267-272.
  6. Wilson W., van Donkelaar C.C., van Rietbergen B., Ito K., Huiskes R., 2004. Stresses in the local collagen network of articular cartilage: a poroviscoelastic fibril-reinforced finite element study. J Biomech 37:357-66
  7. Julkunen P., Kiviranta P., Wilson W., Jurvelin J.S., Korhonen R.K., 2007. Characterization of articular cartilage by combining microscopic analysis with a fibril-reinforced finite-element model. J Biomech 40:1862-70.
  8. Soltz, M.A., Ateshian, G.A., 1998. Experimental verification and theoretical prediction of cartilage interstitial fluid pressurization at an impermeable contact interface in confined compression. J Biomech 31:927–934 (1998)
  9. Komistek, R. D., Stiehl, J. B., Dennis, D. A., Paxson, R. D., Soutas-Little, R. W., 1997. Mathematical model of the lower extremity joint reaction forces using Kane’s method of dynamics. J Biomech 31:185-189.
  10. Kozanek, M., Hosseini, A., Liu, F., Van de Velde, S. K., Gill, T. J., Rubash, H. E., Li, G., 2009. Tibiofemoral kinematics and condylar motion during the stance phase of gait. J Biomech 42:1877-1884.
  11. Räsänen, L.P., Mononen, M.E., Nieminen, M.T., Lammentausta, E., Jurvelin, J.S., Korhonen, R.K., 2012. Implementation of Subject-Specific Collagen Architecture of Cartilage Into a 2D Computational Model of a Knee Joint—Data From the Osteoarthritis Initiative (OAI). J Orthop Res, 2012. DOI 10.1002/jor.22175
  12. Mononen, M.E., Mikkola, M.T., Julkunen, P., Ojala, R., Nieminen, M.T., Jurvelin, J.S., Korhonen, R.K., 2011. Effect of superficial collagen patterns and fibrillation of femoral articular cartilage on knee joint mechanics-a 3D finite element analysis. J Biomech 45:579-87
  13. Mononen, M.E., Julkunen, P., Toyras, J., Jurvelin, J.S., Kiviranta, I., Korhonen, R.K., 2011. Alterations in structure and properties of collagen network of osteoarthritic and repaired cartilage modify knee joint stresses. Biomech Model Mechanobiol 10:357-69.

Publications of our researchers:

  1. Orozco GA, Tanska P, Mononen ME, Halonen KS and Korhonen RK. The effect of constitutive representations and structural constituents of ligaments on knee joint mechanics. Sci Rep. 2018 Feb 2;8(1):2323. doi: 10.1038/s41598-018-20739-w.

  2. Klets O, ORCID:, Mononen ME, Liukkonen MK, Nevalainen MT, Nieminen MT, Saarakkala S and Korhonen RK. Estimation of the Effect of Body Weight on the Development of Osteoarthritis Based on Cumulative Stresses in Cartilage: Data from the Osteoarthritis Initiative. Ann Biomed Eng. 2018 Feb;46(2):334-344. doi: 10.1007/s10439-017-1974-6. Epub 2017

  3. Mononen ME, Tanska P, Isaksson H and Korhonen RK. New algorithm for simulation of proteoglycan loss and collagen degeneration in the knee joint: Data from the osteoarthritis initiative. J Orthop Res. 2017 Nov 18. doi: 10.1002/jor.23811.

  4. Liukkonen MK, Mononen ME, Vartiainen P, Kaukinen P, Bragge T, Suomalainen JS, Malo MKH, Venesmaa S, Kakela P, Pihlajamaki J, Karjalainen PA, Arokoski JP and Korhonen RK. Evaluation of the Effect of Bariatric Surgery-Induced Weight Loss on Knee Gait and Cartilage Degeneration. J Biomech Eng. 2018 Apr 1;140(4). pii: 2662611. doi: 10.1115/1.4038330.

  5. Liukkonen MK, Mononen ME, Tanska P, Saarakkala S, Nieminen MT and Korhonen RK. Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint. Comput Methods Biomech Biomed Engin. 2017 Oct;20(13):1453-1463. doi:

  6. Liukkonen MK, Mononen ME, Klets O, Arokoski JP, Saarakkala S and Korhonen RK. Simulation of Subject-Specific Progression of Knee Osteoarthritis and Comparison to Experimental Follow-up Data: Data from the Osteoarthritis Initiative. Sci Rep. 2017 Aug 23;7(1):9177. doi: 10.1038/s41598-017-09013-7.

  7. Venalainen MS, Mononen ME, Salo J, Rasanen LP, Jurvelin JS, Toyras J, Viren T and Korhonen RK. Quantitative Evaluation of the Mechanical Risks Caused by Focal Cartilage Defects in the Knee. Sci Rep. 2016 Nov 29;6:37538. doi: 10.1038/srep37538.

  8. Klets O, Mononen ME, Tanska P, Nieminen MT, Korhonen RK and Saarakkala S. Comparison of different material models of articular cartilage in 3D computational modeling of the knee: Data from the Osteoarthritis Initiative (OAI). J Biomech. 2016 Dec 8;49(16):3891-3900. doi: 10.1016/j.jbiomech.2016.10.025. Epub

  9. Rasanen LP, Tanska P, Mononen ME, Lammentausta E, Zbyn S, Venalainen MS, Szomolanyi P, van Donkelaar CC, Jurvelin JS, Trattnig S, Nieminen MT and Korhonen RK. Spatial variation of fixed charge density in knee joint cartilage from sodium MRI - Implication on knee joint mechanics under static loading. J Biomech. 2016 Oct 3;49(14):3387-3396. doi: 10.1016/j.jbiomech.2016.09.011. Epub

  10. Halonen KS, Mononen ME, Toyras J, Kroger H, Joukainen A and Korhonen RK. Optimal graft stiffness and pre-strain restore normal joint motion and cartilage responses in ACL reconstructed knee. J Biomech. 2016 Sep 6;49(13):2566-2576. doi: 10.1016/j.jbiomech.2016.05.002. Epub

  11. Halonen KS, Mononen ME, Jurvelin JS, Toyras J, Klodowski A, Kulmala JP and Korhonen RK. Importance of Patella, Quadriceps Forces, and Depthwise Cartilage Structure on Knee Joint Motion and Cartilage Response During Gait. J Biomech Eng. 2016 Jul 1;138(7). pii: 2520868. doi: 10.1115/1.4033516.

  12. Venalainen MS, Mononen ME, Vaananen SP, Jurvelin JS, Toyras J, Viren T and Korhonen RK. Effect of bone inhomogeneity on tibiofemoral contact mechanics during physiological loading. J Biomech. 2016 May 3;49(7):1111-1120. doi: 10.1016/j.jbiomech.2016.02.033. Epub

  13. Mononen ME, Tanska P, Isaksson H and Korhonen RK. A Novel Method to Simulate the Progression of Collagen Degeneration of Cartilage in the Knee: Data from the Osteoarthritis Initiative. Sci Rep. 2016 Feb 24;6:21415. doi: 10.1038/srep21415.

  14. Rasanen LP, Mononen ME, Lammentausta E, Nieminen MT, Jurvelin JS and Korhonen RK. Three dimensional patient-specific collagen architecture modulates cartilage responses in the knee joint during gait. Comput Methods Biomech Biomed Engin. 2016;19(11):1225-40. doi:

  15. Korhonen RK, Tanska P, Kaartinen SM, Fick JM and Mononen ME. New Concept to Restore Normal Cell Responses in Osteoarthritic Knee Joint Cartilage. Exerc Sport Sci Rev. 2015 Jul;43(3):143-52. doi: 10.1249/JES.0000000000000051.

  16. Tanska P, Mononen ME and Korhonen RK. A multi-scale finite element model for investigation of chondrocyte mechanics in normal and medial meniscectomy human knee joint during walking. J Biomech. 2015 Jun 1;48(8):1397-406. doi: 10.1016/j.jbiomech.2015.02.043. Epub

  17. Danso EK, Makela JT, Tanska P, Mononen ME, Honkanen JT, Jurvelin JS, Toyras J, Julkunen P and Korhonen RK. Characterization of site-specific biomechanical properties of human meniscus-Importance of collagen and fluid on mechanical nonlinearities. J Biomech. 2015 Jun 1;48(8):1499-507. doi: 10.1016/j.jbiomech.2015.01.048. Epub

  18. Venalainen MS, Mononen ME, Jurvelin JS, Toyras J, Viren T and Korhonen RK. Importance of material properties and porosity of bone on mechanical response of articular cartilage in human knee joint--a two-dimensional finite element study. J Biomech Eng. 2014 Dec;136(12):121005. doi: 10.1115/1.4028801.

  19. Halonen KS, Mononen ME, Jurvelin JS, Toyras J, Salo J and Korhonen RK. Deformation of articular cartilage during static loading of a knee joint--experimental and finite element analysis. J Biomech. 2014 Jul 18;47(10):2467-74. doi: 10.1016/j.jbiomech.2014.04.013. Epub

  20. Mononen ME, Jurvelin JS and Korhonen RK. Effects of radial tears and partial meniscectomy of lateral meniscus on the knee joint mechanics during the stance phase of the gait cycle--A 3D finite element study. J Orthop Res. 2013 Aug;31(8):1208-17. doi: 10.1002/jor.22358. Epub 2013 Apr 9.

  21. Mononen ME, Jurvelin JS and Korhonen RK. Implementation of a gait cycle loading into healthy and meniscectomised knee joint models with fibril-reinforced articular cartilage. Comput Methods Biomech Biomed Engin. 2015;18(2):141-52. doi:

  22. Halonen KS, Mononen ME, Jurvelin JS, Toyras J and Korhonen RK. Importance of depth-wise distribution of collagen and proteoglycans in articular cartilage--a 3D finite element study of stresses and strains in human knee joint. J Biomech. 2013 Apr 5;46(6):1184-92. doi: 10.1016/j.jbiomech.2012.12.025. Epub

  23. Rasanen LP, Mononen ME, Nieminen MT, Lammentausta E, Jurvelin JS and Korhonen RK. Implementation of subject-specific collagen architecture of cartilage into a 2D computational model of a knee joint--data from the Osteoarthritis Initiative (OAI). J Orthop Res. 2013 Jan;31(1):10-22. doi: 10.1002/jor.22175. Epub 2012 Jul 5.

  24. Mononen ME, Mikkola MT, Julkunen P, Ojala R, Nieminen MT, Jurvelin JS and Korhonen RK. Effect of superficial collagen patterns and fibrillation of femoral articular cartilage on knee joint mechanics-a 3D finite element analysis. J Biomech. 2012 Feb 2;45(3):579-87. doi: 10.1016/j.jbiomech.2011.11.003. Epub

  25. Mononen ME, Julkunen P, Toyras J, Jurvelin JS, Kiviranta I and Korhonen RK. Alterations in structure and properties of collagen network of osteoarthritic and repaired cartilage modify knee joint stresses. Biomech Model Mechanobiol. 2011 Jun;10(3):357-69. doi: 10.1007/s10237-010-0239-1.

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