University of Oulu

Paul O. Bolcos, Mika E. Mononen, Matthew S. Tanaka, Mingrui Yang, Juha-Sampo Suomalainen, Mikko J. Nissi, Juha Töyräs, Benjamin Ma, Xiaojuan Li, Rami K. Korhonen, Identification of locations susceptible to osteoarthritis in patients with anterior cruciate ligament reconstruction: Combining knee joint computational modelling with follow-up T1ρ and T2 imaging, Clinical Biomechanics, Volume 79, 2020, 104844, ISSN 0268-0033, https://doi.org/10.1016/j.clinbiomech.2019.08.004

Identification of locations susceptible to osteoarthritis in patients with anterior cruciate ligament reconstruction: Combining knee joint computational modelling with follow-up T₁ρ and T₂ imaging

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Author: Bolcos, Paul O.1; Mononen, Mika E.1; Tanaka, Matthew S.2;
Organizations: 1Department of Applied Physics, University of Eastern Finland, POB 1627, FI-70211 Kuopio, Finland
2Department of Radiology and Biomedical Imaging, University of California San Francisco, CA-94158 San Francisco, United States of America
3Department of Biomedical Engineering, Cleveland Clinic, OH-44195 Cleveland, United States of America
4Department of Clinical Radiology, Kuopio University Hospital, POB 100, FI-70029 KUH Kuopio, Finland
5Research Unit of Medical Imaging, Physics and Technology, University of Oulu, POB 8000, FI-90014 Oulu, Finland
6Diagnostic Imaging Centre, Kuopio University Hospital, POB 100, FI-70029 KUH Kuopio, Finland
7School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 9.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202101212317
Language: English
Published: Elsevier, 2020
Publish Date: 2021-01-21
Description:

Abstract

Background: Finite element modelling can be used to evaluate altered loading conditions and failure locations in knee joint tissues. One limitation of this modelling approach has been experimental comparison. The aims of this proof-of-concept study were: 1) identify areas susceptible to osteoarthritis progression in anterior cruciate ligament reconstructed patients using finite element modelling; 2) compare the identified areas against changes in T₂ and T₁ₚ values between 1-year and 3-year follow-up timepoints.

Methods: Two patient-specific finite element models of knee joints with anterior cruciate ligament reconstruction were created. The knee geometry was based on clinical magnetic resonance imaging and joint loading was obtained via motion capture. We evaluated biomechanical parameters linked with cartilage degeneration and compared the identified risk areas against T₂ and T₁ₚ maps.

Findings: The risk areas identified by the finite element models matched the follow-up magnetic resonance imaging findings. For Patient 1, excessive values of maximum principal stresses and shear strains were observed in the posterior side of the lateral tibial and femoral cartilage. For Patient 2, high values of maximum principal stresses and shear strains of cartilage were observed in the posterior side of the medial joint compartment. For both patients, increased T₂ and T₁ₚ values between the follow-up times were observed in the same areas.

Interpretation: Finite element models with patient-specific geometries and motions and relatively simple material models of tissues were able to identify areas susceptible to post-traumatic knee osteoarthritis. We suggest that the methodology presented here may be applied in large cohort studies.

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Series: Clinical biomechanics
ISSN: 0268-0033
ISSN-E: 1879-1271
ISSN-L: 0268-0033
Volume: 79
Article number: 104844
DOI: 10.1016/j.clinbiomech.2019.08.004
OADOI: https://oadoi.org/10.1016/j.clinbiomech.2019.08.004
Type of Publication: A1 Journal article – refereed
Field of Science: 3126 Surgery, anesthesiology, intensive care, radiology
Subjects:
Funding: This project has received funding from the Doctoral Programme in Science, Technology and Computing (SCITECO) of the University of Eastern Finland, the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 755037), Academy of Finland (grants 285909, 307932 and 286526), Sigrid Jusélius Foundation, and National Institutes of Health (NIH/NIAMS P50 AR060752). CSC-IT Center for Science, Finland, is acknowledged for providing computing resources.
Copyright information: © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/)
  https://creativecommons.org/licenses/by-nc-nd/4.0/