1. An Automated Pipeline for Analysing Contact
Stresses in the Carpometacarpal Joint
M.T.Y. Schneider1, J. Crisco2, A. C. Weiss2, A. L. Ladd3, P. Nielsen1,4, T. Besier1,4, J. Zhang1
1. Auckland Bioengineering Institute, The University of Auckland, 2. Department of Orthopaedics, Brown University, USA,
3. Department of Orthopaedic Surgery, Stanford University, USA, 4. Department of Engineering Science, The University of Auckland,
Introduction
• Carpometacarpal (CMC) joint osteoarthritis (OA) is a serious and pervasive disease, affecting 15% of adults over 30
years and two to six times more women than men [1].
• Sexual dimorphism, kinematics and their effects on joint biomechanics and resulting cartilage stresses are implicated
with the pathogenesis of CMC OA [2].
• Here we present an automated pipeline for creating finite element (FE) models of the CMC joint for analysing cartilage
contact stresses during two isometric functional tasks: pinch and grasp.
Conclusion
• This pipeline shows promise for automatically collecting a large population of CMC joint stresses
for statistical analysis.
• Eventually, it may be used in a clinical setting where analysis of joint stresses could be
invaluable to diagnosis, functional analysis, disease prevention, and treatment.
Future Work
• We are currently testing this method on a large data set of CMC joints performing functional
tasks including pinch, grasp, jar twist.
• Improved imaging data of the cartilage will allow us to remove the current limitation of uniform
thickness.
• The inclusion of muscle models and true ligaments in the model will allow this pipeline to be
used for other analyses, such as analysis of muscle forces, or dynamic stresses during motion.
• Mesh refinement validation, and validation of stresses calculated needs to be performed.
Acknowledgements
This work was supported by the National Institute of Arthritis and Musculoskeletal and Skin
Diseases of the National Institutes of Health and the Auckland Bioengineering Institute.
References
1. Cootes, T.F., et al., Robust and accurate shape model fitting using random forest regression voting, in Computer Vision–ECCV 2012.
012, Springer. p. 278-291.
2. Zhang, J., Malcolm, D., Hislop-Jambrich, J., Thomas, C. D. L., & Nielsen, P. M. F. (2014). An anatomical region-based statistical
shape model of the human femur. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–10.
doi:10.1080/21681163.2013.878668
Methods
Statistical Shape Model
A training set of 50 CT images (age
range: 18 yrs to 67 yrs; 24 females
and 26 males) were used to train a
statistical shape model.
A. The CT image is passed to automatic
segmentation tool.
B. Mesh fitting was restricted by the
statistical shape model.
C. The CMC joint was segmented by fitting
the mean CMC mesh to nodal locations
predicted by the regressors.
D. Model Setup
1. 3D hexahedral cartilage mesh was
generated based on embedded
nodes.
2. Overclosure detection is used to
prepare the meshes for the solver.
3. Discrete elements are automatically
generated around the joint.
4. A least-squares optimizer then finds
optimal parameters that minimizes
displacements between landmarks
from simulation and CT-data.
A
B
C
D
Results
• The pipeline was able to determine the cartilage stresses during functional tasks using a force-
controlled finite element simulation.
• Results below show stresses in the trapezial cartilage during key pinch and grasp tasks. Final
positions of the metacarpal were within 0.06 mm rms from imaged positions.
Pinch
Effective Stress
Grasp
Mode 1:
−2σ
0σ
+2σ