gms | German Medical Science

58. Kongress der Deutschen Gesellschaft für Handchirurgie

Deutsche Gesellschaft für Handchirurgie

12. - 14.10.2017, München

A computational musculoskeletal model of the PIP joint generated with anatomical data from MR, CT and optical motion capture

Meeting Abstract

  • corresponding author presenting/speaker David Warwick - Faculty of Medicine, University of Southampton, Southampton, United Kingdom
  • Chris Phillips - University of Southampton, Faculty of Engineering, Southampton, United Kingdom
  • Alex Dickinson - University of Southampton, Faculty of Engineering, Southampton, United Kingdom
  • Cheryl Metcalf - University of Southampton, Faculty of Health Sciences, Southampton, United Kingdom
  • Leonard King - University Hospital Southampton, Radiology, Southampton, United Kingdom

Deutsche Gesellschaft für Handchirurgie. 58. Kongress der Deutschen Gesellschaft für Handchirurgie. München, 12.-14.10.2017. Düsseldorf: German Medical Science GMS Publishing House; 2017. Doc17dgh039

doi: 10.3205/17dgh039, urn:nbn:de:0183-17dgh0390

Published: October 10, 2017

© 2017 Warwick et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

Text

Objectives: The PIP joint is complex, with movement & stability controlled by bone contour, ligament tension & a kinetic chain of intrinsic & extrinsic tendons. PIP joint replacement has an uncertain outcome & high failure rate, compromised by

  • disruption to normal anatomy by disease
  • surgical inaccuracy in reproducing joint kinematics
  • imperfect prosthetic joint geometry

Better understanding of the complex joint dynamics in health, disease & after arthroplasty through computational modelling will contribute to improved outcomes.

Method: We are developing a computational musculoskeletal model of the index finger which includes

  • tendon-muscle: intrinsic & extrinsic muscles
  • ligaments: ulnar & radial collateral ligaments, volar plates & retinacular ligaments
  • bone: distal end of ulna & radius, carpal bones & phalanges.

In 9 healthy volunteers, we obtained bone & soft tissue data from CT & MR and hand kinematics from optical motion capture. All data were acquired in extension, partial flexion & flexion, standardised with 3D printed jig.

Using enhanced motion capture markers and AnyBody's force dependent kinematics (FDK), the simulated PIPJ model includes rotations & translations, thus more physiologically accurate than existing models.

Results: We will present high resolution dynamic 3D images representing the complexities of the PIPJ & its supporting soft tissue structures.

Conclusion: This model will allow better understanding of the PIPJ in health & disease. Pre-operative modelling will inform more accurate surgical cuts & even bespoke implant design. Post-operative analysis of failed arthroplasty will lead to better understanding of the bone & soft tissue imbalances and implant flaws which contribute to failure.