Computational model-based probabilistic analysis of in vivo material properties for ligament stiffness using the laxity test and computed tomography

  • Kyoung-Tak Kang
  • Sung-Hwan Kim
  • Juhyun Son
  • Young Han Lee
  • Heoung-Jae ChunEmail author
Clinical Applications of Biomaterials Original Research
Part of the following topical collections:
  1. Clinical Applications of Biomaterials


The objective of this paper was to evaluate in vivo material properties in order to address technical aspects of computational modeling of ligaments in the tibiofemoral joint using a probabilistic method. The laxity test was applied to the anterior-posterior drawer under 30° and 90° of flexion with a series of stress radiographs, a Telos device, and computed tomography. Ligament stiffness was investigated using sensitivity analysis based on the Monte-Carlo method with a subject-specific finite element model generated from in vivo computed tomography and magnetic resonance imaging data, subjected to laxity test conditions. The material properties of ligament stiffness and initial ligament strain in a subject-specific finite element model were optimized to minimize the differences between the movements of the tibia and femur in the finite element model and the computed tomography images in the laxity test. The posterior cruciate ligament was the most significant factor in flexion and posterior drawer, while the anterior cruciate ligament primarily was the most significant factor for the anterior drawer. The optimized material properties model predictions in simulation and the laxity test were more accurate than predictions based on the initial material properties in subject-specific computed tomography measurement. Thus, this study establishes a standard for future designs in allograft, xenograft, and artificial ligaments for anterior cruciate ligament and posterior cruciate ligament injuries.


Anterior Cruciate Ligament Finite Element Model Posterior Cruciate Ligament Intraclass Correlation Coefficient Knee Laxity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

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Supplementary Information


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Kyoung-Tak Kang
    • 1
  • Sung-Hwan Kim
    • 2
  • Juhyun Son
    • 1
  • Young Han Lee
    • 3
  • Heoung-Jae Chun
    • 1
    Email author
  1. 1.Department of Mechanical EngineeringYonsei UniversitySeodaemun-guRepublic of Korea
  2. 2.Department of Orthopedic Surgery, Arthroscopy and Joint Research InstituteYonsei University College of Medicine, Gangnam Severance HospitalGangnam-guRepublic of Korea
  3. 3.Department of Radiology, Research Institute of Radiological Science, Medical Convergence Research Institute, and Severance Biomedical Science InstituteYonsei University College of MedicineSeodaemun-guRepublic of Korea

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