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Jahangir S, Bosch W, Esrafilian A, Mononen ME, Tanska P, Stenroth L, Henriksen M, Alkjær T, Korhonen RK. Effect of uncertainties in musculoskeletal modeling inputs on sensitivity of knee joint finite element simulations. Med Eng Phys 2025; 138:104313. [PMID: 40180526 DOI: 10.1016/j.medengphy.2025.104313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 01/24/2025] [Accepted: 02/24/2025] [Indexed: 04/05/2025]
Abstract
Musculoskeletal finite element modeling is used to estimate mechanical responses of knee joint tissues but involves uncertainties in muscle activations, marker locations, cartilage stiffness, maximum isometric forces, and gait parameter personalization. This study investigates how these uncertainties affect cartilage mechanical responses in knee joint finite element models during walking. We selected three subjects and constructed five musculoskeletal models for each, representing different variations of modeling assumptions, along with a reference model using conventional assumptions. We then ran finite element simulations of knee joints using both personalized gait inputs (motion and loading boundary conditions) and non-personalized gait inputs from literature. Our results demonstrated that varying modeling assumptions, such as optimization function for muscle activation patterns, knee marker position, knee cartilage stiffness, and maximum isometric force, produced highly subject-specific effects. Differences between the reference and altered models ranged from 3% to 30% in musculoskeletal modeling and from 1% to 61% in finite element modeling results. The largest effects occurred with non-personalized gait data, resulting in up to 6- and 2-fold changes in musculoskeletal and finite element modeling results, respectively. This study highlights the sensitivity of knee mechanics to different modeling assumptions and underscores the importance of applying personalized gait parameters for accurate finite element simulations.
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Affiliation(s)
- Sana Jahangir
- Department of Technical Physics, University of Eastern Finland, Finland.
| | - Will Bosch
- Department of Technical Physics, University of Eastern Finland, Finland.
| | - Amir Esrafilian
- Department of Technical Physics, University of Eastern Finland, Finland
| | - Mika E Mononen
- Department of Technical Physics, University of Eastern Finland, Finland
| | - Petri Tanska
- Department of Technical Physics, University of Eastern Finland, Finland
| | - Lauri Stenroth
- Department of Technical Physics, University of Eastern Finland, Finland
| | - Marius Henriksen
- The Parker Institute, Copenhagen University Hospital, Bispebjerg, Frederiksberg, Denmark
| | - Tine Alkjær
- The Parker Institute, Copenhagen University Hospital, Bispebjerg, Frederiksberg, Denmark; Department of Biomedical Sciences, University of Copenhagen, Denmark
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, Finland.
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Kakavand R, Tahghighi P, Ahmadi R, Edwards WB, Komeili A. Swin UNETR Segmentation with Automated Geometry Filtering for Biomechanical Modeling of Knee Joint Cartilage. Ann Biomed Eng 2025; 53:908-922. [PMID: 39789362 DOI: 10.1007/s10439-024-03675-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Accepted: 12/29/2024] [Indexed: 01/12/2025]
Abstract
PURPOSE Simulation studies, such as finite element (FE) modeling, offer insights into knee joint biomechanics, which may not be achieved through experimental methods without direct involvement of patients. While generic FE models have been used to predict tissue biomechanics, they overlook variations in population-specific geometry, loading, and material properties. In contrast, subject-specific models account for these factors, delivering enhanced predictive precision but requiring significant effort and time for development. METHODS This study aimed to facilitate subject-specific knee joint FE modeling by integrating an automated cartilage segmentation algorithm using a 3D Swin UNETR. This algorithm provided initial segmentation of knee cartilage, followed by automated geometry filtering to refine surface roughness and continuity. In addition to the standard metrics of image segmentation performance, such as Dice similarity coefficient (DSC) and Hausdorff distance, the method's effectiveness was also assessed in FE simulation. Nine pairs of knee cartilage FE models, using manual and automated segmentation methods, were developed to compare the predicted stress and strain responses during gait. RESULTS The automated segmentation achieved high Dice similarity coefficients of 89.4% for femoral and 85.1% for tibial cartilage, with a Hausdorff distance of 2.3 mm between the automated and manual segmentation. Mechanical results including maximum principal stress and strain, fluid pressure, fibril strain, and contact area showed no significant differences between the manual and automated FE models. CONCLUSION These findings demonstrate the effectiveness of the proposed automated segmentation method in creating accurate knee joint FE models. The automated models developed in this study have been made publicly accessible to support biomechanical modeling and medical image segmentation studies ( https://data.mendeley.com/datasets/dc832g7j5m/1 ).
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Affiliation(s)
- Reza Kakavand
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Peyman Tahghighi
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Reza Ahmadi
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - W Brent Edwards
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB, T2N 1N4, Canada
| | - Amin Komeili
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
- McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada.
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB, T2N 1N4, Canada.
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Mohout I, Elahi SA, Esrafilian A, Killen BA, Korhonen RK, Verschueren S, Jonkers I. Signatures of disease progression in knee osteoarthritis: insights from an integrated multi-scale modeling approach, a proof of concept. Front Bioeng Biotechnol 2023; 11:1214693. [PMID: 37576991 PMCID: PMC10413555 DOI: 10.3389/fbioe.2023.1214693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 07/13/2023] [Indexed: 08/15/2023] Open
Abstract
Introduction: Knee osteoarthritis (KOA) is characterized by articular cartilage degeneration. It has been widely accepted that the mechanical joint environment plays a significant role in the onset and progression of this disease. In silico models have been used to study the interplay between mechanical loading and cartilage degeneration, hereby relying mainly on two key mechanoregulatory factors indicative of collagen degradation and proteoglycans depletion. These factors are the strain in collagen fibril direction (SFD) and maximum shear strain (MSS) respectively. Methods: In this study, a multi-scale in silico modeling approach was used based on a synergy between musculoskeletal and finite element modeling to evaluate the SFD and MSS. These strains were evaluated during gait based on subject-specific gait analysis data collected at baseline (before a 2-year follow-up) for a healthy and progressive early-stage KOA subject with similar demographics. Results: The results show that both SFD and MSS factors allowed distinguishing between a healthy subject and a KOA subject, showing progression at 2 years follow-up, at the instance of peak contact force as well as during the stance phase of the gait cycle. At the peak of the stance phase, the SFD were found to be more elevated in the KOA patient with the median being 0.82% higher in the lateral and 0.4% higher in the medial compartment of the tibial cartilage compared to the healthy subject. Similarly, for the MSS, the median strains were found to be 3.6% higher in the lateral and 0.7% higher in the medial tibial compartment of the KOA patient compared to the healthy subject. Based on these intersubject SFD and MSS differences, we were additionally able to identify that the tibial compartment of the KOA subject at risk of progression. Conclusion/discussion: We confirmed the mechanoregulatory factors as potential biomarkers to discriminate patients at risk of disease progression. Future studies should evaluate the sensitivity of the mechanoregulatory factors calculated based on this multi-scale modeling workflow in larger patient and control cohorts.
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Affiliation(s)
- Ikram Mohout
- Department of Movement Science, Human Movement Biomechanics Research Group, Leuven, Belgium
| | - Seyed Ali Elahi
- Department of Movement Science, Human Movement Biomechanics Research Group, Leuven, Belgium
- Mechanical Engineering Department, Soft Tissue Biomechanics Group, Leuven, Belgium
| | - Amir Esrafilian
- Department of Technical Physics, Biophysics of Bone and Cartilage Research Group, University of Eastern Finland, Kuopio, Finland
| | - Bryce A. Killen
- Department of Movement Science, Human Movement Biomechanics Research Group, Leuven, Belgium
| | - Rami K. Korhonen
- Department of Technical Physics, Biophysics of Bone and Cartilage Research Group, University of Eastern Finland, Kuopio, Finland
| | - Sabine Verschueren
- Department of Rehabilitation Science, Research Group for Musculoskeletal Rehabilitation, Leuven, Belgium
| | - Ilse Jonkers
- Department of Movement Science, Human Movement Biomechanics Research Group, Leuven, Belgium
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van Donkelaar CC. The potential contribution of in silico studies to improved treatment of osteoarthritis. Nat Rev Rheumatol 2023; 19:261-262. [PMID: 36949105 DOI: 10.1038/s41584-023-00953-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Affiliation(s)
- Corrinus C van Donkelaar
- Orthopaedic Biomechanics, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
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Elahi SA, Castro-Viñuelas R, Tanska P, Korhonen RK, Lories R, Famaey N, Jonkers I. Contribution of collagen degradation and proteoglycan depletion to cartilage degeneration in primary and secondary osteoarthritis: an in silico study. Osteoarthritis Cartilage 2023; 31:741-752. [PMID: 36669584 DOI: 10.1016/j.joca.2023.01.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/13/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023]
Abstract
OBJECTIVES Current experimental approaches cannot elucidate the effect of maladaptive changes on the main cartilage constituents during the degeneration process in osteoarthritis (OA). In silico approaches, however, allow creating 'virtual knock-out' cases to elucidate these effects in a constituent-specific manner. We used such an approach to study the main mechanisms of cartilage degeneration in different mechanical loadings associated with the following OA etiologies: (1) physiological loading of degenerated cartilage, (2) injurious loading of healthy intact cartilage and (3) physiological loading of cartilage with a focal defect. METHODS We used the recently developed Cartilage Adaptive REorientation Degeneration (CARED) framework to simulate cartilage degeneration associated with primary and secondary OA (OA cases (1)-(3)). CARED incorporates numerical description of tissue-level cartilage degeneration mechanisms in OA, namely, collagen degradation, collagen reorientation, fixed charged density loss and tissue hydration increase following mechanical loading. We created 'virtual knock-out' scenarios by deactivating these degenerative processes one at a time in each of the three OA cases. RESULTS In the injurious loading of intact and physiological loading of degenerated cartilage, collagen degradation drives degenerative changes through fixed charge density loss and tissue hydration rise. In contrast, the two later mechanisms were more prominent in the focal defect cartilage model. CONCLUSION The virtual knock-out models reveal that injurious loading to intact cartilage and physiological loading to degenerated cartilage induce initial degenerative changes in the collagen network, whereas, in the presence of a focal cartilage defect, mechanical loading initially causes proteoglycans (PG) depletion, before changes in the collagen fibril network occur.
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Affiliation(s)
- S A Elahi
- Department of Movement Sciences, Human Movement Biomechanics Research Group, KU Leuven, Leuven, Belgium; Mechanical Engineering Department, Biomechanics Section, Soft Tissue Biomechanics Group, KU Leuven, Leuven, Belgium.
| | - R Castro-Viñuelas
- Department of Movement Sciences, Human Movement Biomechanics Research Group, KU Leuven, Leuven, Belgium; Department of Development and Regeneration, Skeletal Biology and Engineering Research Centre, Laboratory of Tissue Homeostasis and Disease, KU Leuven, Leuven, Belgium.
| | - P Tanska
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - R K Korhonen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - R Lories
- Department of Development and Regeneration, Skeletal Biology and Engineering Research Centre, Laboratory of Tissue Homeostasis and Disease, KU Leuven, Leuven, Belgium; Division of Rheumatology, University Hospitals Leuven, Leuven, Belgium.
| | - N Famaey
- Mechanical Engineering Department, Biomechanics Section, Soft Tissue Biomechanics Group, KU Leuven, Leuven, Belgium.
| | - I Jonkers
- Department of Movement Sciences, Human Movement Biomechanics Research Group, KU Leuven, Leuven, Belgium; Department of Development and Regeneration, Skeletal Biology and Engineering Research Centre, Laboratory of Tissue Homeostasis and Disease, KU Leuven, Leuven, Belgium.
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A numerical model for fibril remodeling in articular cartilage. Knee 2023; 41:83-96. [PMID: 36642036 DOI: 10.1016/j.knee.2022.12.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/05/2022] [Accepted: 12/14/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Collagen fibrils of articular cartilage have a distinct organization in mature human knee joints. It seems that a mechanobiological process drives the remodeling of newborn collagen fibrils with maturation. Therefore, the goal of the present study was to develop a collagen fibril remodeling algorithm that describes the unique collagen fibril organization in a 3D knee model. METHOD A fibril-reinforced, biphasic cartilage model was used with a cuboid and a 3D human knee joint geometries. An isotropic collagen fibril distribution was assigned to the cartilage at the start of the analysis. Each fibril was rotated towards the direction that resulted in a maximum stretch at each time increment of the loading cycle. RESULTS The resulting pattern for the collagen fibrils was compared with split line patterns of porcine knee joint cartilage and also data published in the literature. Fibrils on the articular surface had a radial pattern towards the geometrical centroid of the tibial and femoral cartilage. In the tibiofemoral contact regions of superficial zone, fibrils were oriented circumferentially and randomly. In the porcine samples, the split-line patterns were similar to those obtained theoretically. Depth-wise organization of fibril network was characterized by fibrils perpendicular to the subchondral bone in the deeper layers, and fibrils parallel to the surface of cartilage in the superficial zone. CONCLUSIONS The maximum stretch criterion, coupled with a biphasic constitutive model, successfully predicted the collagen fibril organization observed in the articular cartilage throughout the depth and on the articular surface.
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A musculoskeletal finite element model of rat knee joint for evaluating cartilage biomechanics during gait. PLoS Comput Biol 2022; 18:e1009398. [PMID: 35657996 PMCID: PMC9166403 DOI: 10.1371/journal.pcbi.1009398] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 04/26/2022] [Indexed: 12/02/2022] Open
Abstract
Abnormal loading of the knee due to injuries or obesity is thought to contribute to the development of osteoarthritis (OA). Small animal models have been used for studying OA progression mechanisms. However, numerical models to study cartilage responses under dynamic loading in preclinical animal models have not been developed. Here we present a musculoskeletal finite element model of a rat knee joint to evaluate cartilage biomechanical responses during a gait cycle. The rat knee joint geometries were obtained from a 3-D MRI dataset and the boundary conditions regarding loading in the joint were extracted from a musculoskeletal model of the rat hindlimb. The fibril-reinforced poroelastic (FRPE) properties of the rat cartilage were derived from data of mechanical indentation tests. Our numerical results showed the relevance of simulating anatomical and locomotion characteristics in the rat knee joint for estimating tissue responses such as contact pressures, stresses, strains, and fluid pressures. We found that the contact pressure and maximum principal strain were virtually constant in the medial compartment whereas they showed the highest values at the beginning of the gait cycle in the lateral compartment. Furthermore, we found that the maximum principal stress increased during the stance phase of gait, with the greatest values at midstance. We anticipate that our approach serves as a first step towards investigating the effects of gait abnormalities on the adaptation and degeneration of rat knee joint tissues and could be used to evaluate biomechanically-driven mechanisms of the progression of OA as a consequence of joint injury or obesity. Osteoarthritis is a disease of the musculoskeletal system which is characterized by the degradation of articular cartilage. Changes in the knee loading after injuries or obesity contribute to the development of cartilage degeneration. Since injured cartilage cannot be reversed back to intact conditions, small animal models have been widely used for investigating osteoarthritis progression mechanisms. Moreover, experimental studies have been complemented with numerical models to overcome inherent limitations such as cost, difficulties to obtain accurate measures and replicate degenerative situations in the knee joint. However, computational models to study articular cartilage responses under dynamic loading in small animal models have not been developed. Thus, here we present a musculoskeletal finite element model (MSFE) of a rat knee joint to evaluate cartilage biomechanical responses during gait. Our computational model considers both the anatomical and locomotion characteristics of the rat knee joint for estimating mechanical responses in the articular cartilage. We suggest that our approach can be used to investigate tissue adaptations based on the mechanobiological responses of the cartilage to prevent the progression of osteoarthritis.
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Rapid X-Ray-Based 3-D Finite Element Modeling of Medial Knee Joint Cartilage Biomechanics During Walking. Ann Biomed Eng 2022; 50:666-679. [PMID: 35262835 PMCID: PMC9079039 DOI: 10.1007/s10439-022-02941-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 02/23/2022] [Indexed: 11/30/2022]
Abstract
Finite element (FE) modeling is becoming an increasingly popular method for analyzing knee joint mechanics and biomechanical mechanisms leading to osteoarthritis (OA). The most common and widely available imaging method for knee OA diagnostics is planar X-ray imaging, while more sophisticated imaging methods, e.g., magnetic resonance imaging (MRI) and computed tomography (CT), are seldom used. Hence, the capability to produce accurate biomechanical knee joint models directly from X-ray imaging would bring FE modeling closer to clinical use. Here, we extend our atlas-based framework by generating FE knee models from X-ray images (N = 28). Based on measured anatomical landmarks from X-ray and MRI, knee joint templates were selected from the atlas library. The cartilage stresses and strains of the X-ray-based model were then compared with the MRI-based model during the stance phase of the gait. The biomechanical responses were statistically not different between MRI- vs. X-ray-based models when the template obtained from X-ray imaging was the same as the MRI template. However, if this was not the case, the peak values of biomechanical responses were statistically different between X-ray and MRI models. The developed X-ray-based framework may pave the way for a clinically feasible approach for knee joint FE modeling.
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