An Automated and Robust Tool for Musculoskeletal and Finite Element Modeling of the Knee Joint

Objective: To develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline. Methods: Magnetic resonance images (MRIs) were used to train nnU-Net networks for auto-segmentation of knee bones (femur, tibia, patella, and fibula), cartilages (femur, tibia, an...

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Published inIEEE transactions on biomedical engineering Vol. 72; no. 1; pp. 56 - 69
Main Authors Esrafilian, Amir, Chandra, Shekhar S., Gatti, Anthony A., Nissi, Mikko J., Mustonen, Anne-Mari, Saisanen, Laura, Reijonen, Jusa, Nieminen, Petteri, Julkunen, Petro, Toyras, Juha, Saxby, David J., Lloyd, David G., Korhonen, Rami K.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adult
Biological system modeling
Biomechanical Phenomena - physiology
Biomechanics
Bones
Cartilage
Cartilage mechanics
Customization
Degrees of freedom
Elastic foundations
Female
Femur
Fibula
Finite Element Analysis
Finite element method
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
In vivo methods and tests
Iron
Kinematics
Knee
Knee Joint - anatomy & histology
Knee Joint - diagnostic imaging
Knee Joint - physiology
knee osteoarthritis
Ligaments
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
mechanobiology
Meshing
Modelling
Models, Biological
multiscale modeling
Patella
Robustness
Shear strain
Strain
Tibia
Young Adult
Online AccessGet full text
ISSN0018-9294
1558-2531
1558-2531
DOI10.1109/TBME.2024.3438272

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Abstract Objective: To develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline. Methods: Magnetic resonance images (MRIs) were used to train nnU-Net networks for auto-segmentation of knee bones (femur, tibia, patella, and fibula), cartilages (femur, tibia, and patella), menisci, and major knee ligaments. Two different MRI sequences were used to broaden applicability. Next, we created MSK-FE models of an unseen dataset using two MSK-FE modeling pipelines: template-based and auto-meshing. MSK models had personalized knee geometries with multi-degree-of-freedom elastic foundation contacts. FE models used fibril-reinforced poroviscoelastic swelling material models for cartilages and menisci. Results: Volumes of knee bones, cartilages, and menisci did not significantly differ ( p >0.05) across MRI sequences. MSK models estimated secondary knee kinematics during passive knee flexion tests consistent with in vivo and simulation-based values from the literature. Between the template-based and auto-meshing FE models, estimated cartilage mechanics often differed significantly ( p <0.05), though differences were <15% (considering peaks during walking), i.e., <1.5 MPa for maximum principal stress, <1 percentage point for collagen fibril strain, and <3 percentage points for maximum shear strain. Conclusion: The template-based modeling provided a more rapid and robust tool than the auto-meshing approach, while the estimated knee biomechanics were comparable. Nonetheless, the auto-meshing approach might provide more accurate estimates in subjects with distinct knee irregularities, e.g., cartilage lesions. Significance: The MSK-FE modeling tool provides a rapid, easy-to-use, and robust approach for investigating task- and person-specific mechanical responses of the knee cartilage and menisci, holding significant promise, e.g., in personalized rehabilitation planning.
AbstractList To develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline.OBJECTIVETo develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline.Magnetic resonance images (MRIs) were used to train nnU-Net networks for auto-segmentation of knee bones (femur, tibia, patella, and fibula), cartilages (femur, tibia, and patella), menisci, and major knee ligaments. Two different MRI sequences were used to broaden applicability. Next, we created MSK-FE models of an unseen dataset using two MSK-FE modeling pipelines: template-based and auto-meshing. MSK models had personalized knee geometries with multi-degree-of-freedom elastic foundation contacts. FE models used fibril-reinforced poroviscoelastic swelling material models for cartilages and menisci.METHODSMagnetic resonance images (MRIs) were used to train nnU-Net networks for auto-segmentation of knee bones (femur, tibia, patella, and fibula), cartilages (femur, tibia, and patella), menisci, and major knee ligaments. Two different MRI sequences were used to broaden applicability. Next, we created MSK-FE models of an unseen dataset using two MSK-FE modeling pipelines: template-based and auto-meshing. MSK models had personalized knee geometries with multi-degree-of-freedom elastic foundation contacts. FE models used fibril-reinforced poroviscoelastic swelling material models for cartilages and menisci.Volumes of knee bones, cartilages, and menisci did not significantly differ (p>0.05) across MRI sequences. MSK models estimated secondary knee kinematics during passive knee flexion tests consistent with in vivo and simulation-based values from the literature. Between the template-based and auto-meshing FE models, estimated cartilage mechanics often differed significantly (p<0.05), though differences were <15% (considering peaks during walking), i.e., <1.5 MPa for maximum principal stress, <1 percentage point for collagen fibril strain, and <3 percentage points for maximum shear strain.RESULTSVolumes of knee bones, cartilages, and menisci did not significantly differ (p>0.05) across MRI sequences. MSK models estimated secondary knee kinematics during passive knee flexion tests consistent with in vivo and simulation-based values from the literature. Between the template-based and auto-meshing FE models, estimated cartilage mechanics often differed significantly (p<0.05), though differences were <15% (considering peaks during walking), i.e., <1.5 MPa for maximum principal stress, <1 percentage point for collagen fibril strain, and <3 percentage points for maximum shear strain.The template-based modeling provided a more rapid and robust tool than the auto-meshing approach, while the estimated knee biomechanics were comparable. Nonetheless, the auto-meshing approach might provide more accurate estimates in subjects with distinct knee irregularities, e.g., cartilage lesions.CONCLUSIONThe template-based modeling provided a more rapid and robust tool than the auto-meshing approach, while the estimated knee biomechanics were comparable. Nonetheless, the auto-meshing approach might provide more accurate estimates in subjects with distinct knee irregularities, e.g., cartilage lesions.The MSK-FE modeling tool provides a rapid, easy-to-use, and robust approach for investigating task- and person-specific mechanical responses of the knee cartilage and menisci, holding significant promise, e.g., in personalized rehabilitation planning.SIGNIFICANCEThe MSK-FE modeling tool provides a rapid, easy-to-use, and robust approach for investigating task- and person-specific mechanical responses of the knee cartilage and menisci, holding significant promise, e.g., in personalized rehabilitation planning.
To develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline. Magnetic resonance images (MRIs) were used to train nnU-Net networks for auto-segmentation of knee bones (femur, tibia, patella, and fibula), cartilages (femur, tibia, and patella), menisci, and major knee ligaments. Two different MRI sequences were used to broaden applicability. Next, we created MSK-FE models of an unseen dataset using two MSK-FE modeling pipelines: template-based and auto-meshing. MSK models had personalized knee geometries with multi-degree-of-freedom elastic foundation contacts. FE models used fibril-reinforced poroviscoelastic swelling material models for cartilages and menisci. Volumes of knee bones, cartilages, and menisci did not significantly differ (p>0.05) across MRI sequences. MSK models estimated secondary knee kinematics during passive knee flexion tests consistent with in vivo and simulation-based values from the literature. Between the template-based and auto-meshing FE models, estimated cartilage mechanics often differed significantly (p<0.05), though differences were <15% (considering peaks during walking), i.e., <1.5 MPa for maximum principal stress, <1 percentage point for collagen fibril strain, and <3 percentage points for maximum shear strain. The template-based modeling provided a more rapid and robust tool than the auto-meshing approach, while the estimated knee biomechanics were comparable. Nonetheless, the auto-meshing approach might provide more accurate estimates in subjects with distinct knee irregularities, e.g., cartilage lesions. The MSK-FE modeling tool provides a rapid, easy-to-use, and robust approach for investigating task- and person-specific mechanical responses of the knee cartilage and menisci, holding significant promise, e.g., in personalized rehabilitation planning.
Objective: To develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline. Methods: Magnetic resonance images (MRIs) were used to train nnU-Net networks for auto-segmentation of knee bones (femur, tibia, patella, and fibula), cartilages (femur, tibia, and patella), menisci, and major knee ligaments. Two different MRI sequences were used to broaden applicability. Next, we created MSK-FE models of an unseen dataset using two MSK-FE modeling pipelines: template-based and auto-meshing. MSK models had personalized knee geometries with multi-degree-of-freedom elastic foundation contacts. FE models used fibril-reinforced poroviscoelastic swelling material models for cartilages and menisci. Results: Volumes of knee bones, cartilages, and menisci did not significantly differ ( p >0.05) across MRI sequences. MSK models estimated secondary knee kinematics during passive knee flexion tests consistent with in vivo and simulation-based values from the literature. Between the template-based and auto-meshing FE models, estimated cartilage mechanics often differed significantly ( p <0.05), though differences were <15% (considering peaks during walking), i.e., <1.5 MPa for maximum principal stress, <1 percentage point for collagen fibril strain, and <3 percentage points for maximum shear strain. Conclusion: The template-based modeling provided a more rapid and robust tool than the auto-meshing approach, while the estimated knee biomechanics were comparable. Nonetheless, the auto-meshing approach might provide more accurate estimates in subjects with distinct knee irregularities, e.g., cartilage lesions. Significance: The MSK-FE modeling tool provides a rapid, easy-to-use, and robust approach for investigating task- and person-specific mechanical responses of the knee cartilage and menisci, holding significant promise, e.g., in personalized rehabilitation planning.
Author Gatti, Anthony A.
Mustonen, Anne-Mari
Reijonen, Jusa
Toyras, Juha
Chandra, Shekhar S.
Saisanen, Laura
Julkunen, Petro
Esrafilian, Amir
Saxby, David J.
Nieminen, Petteri
Korhonen, Rami K.
Lloyd, David G.
Nissi, Mikko J.
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Snippet Objective: To develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline. Methods: Magnetic resonance images...
To develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline. Magnetic resonance images (MRIs) were used to...
To develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline.OBJECTIVETo develop and assess an automatic and...
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StartPage 56
SubjectTerms Adult
Biological system modeling
Biomechanical Phenomena - physiology
Biomechanics
Bones
Cartilage
Cartilage mechanics
Customization
Degrees of freedom
Elastic foundations
Female
Femur
Fibula
Finite Element Analysis
Finite element method
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
In vivo methods and tests
Iron
Kinematics
Knee
Knee Joint - anatomy & histology
Knee Joint - diagnostic imaging
Knee Joint - physiology
knee osteoarthritis
Ligaments
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
mechanobiology
Meshing
Modelling
Models, Biological
multiscale modeling
Patella
Robustness
Shear strain
Strain
Tibia
Young Adult
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Title An Automated and Robust Tool for Musculoskeletal and Finite Element Modeling of the Knee Joint
URI https://ieeexplore.ieee.org/document/10666719
https://www.ncbi.nlm.nih.gov/pubmed/39236141
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