A computational algorithm to simulate disorganization of collagen network in injured articular cartilage

Signatures of disease progression in knee osteoarthritis: insights from an integrated multi-scale modeling approach, a proof of concept

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.

Contribution of collagen degradation and proteoglycan depletion to cartilage degeneration in primary and secondary osteoarthritis: an in silico study

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.

A numerical model for fibril remodeling in articular cartilage

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.

Multiscale In Silico Modeling of Cartilage Injuries

Injurious loading of the joint can be accompanied by articular cartilage damage and trigger inflammation. However, it is not well-known which mechanism controls further cartilage degradation, ultimately leading to post-traumatic osteoarthritis. For personalized prognostics, there should also be a method that can predict tissue alterations following joint and cartilage injury. This chapter gives an overview of experimental and computational methods to characterize and predict cartilage degradation following joint injury. Two mechanisms for cartilage degradation are proposed. In (1) biomechanically driven cartilage degradation, it is assumed that excessive levels of strain or stress of the fibrillar or non-fibrillar matrix lead to proteoglycan loss or collagen damage and degradation. In (2) biochemically driven cartilage degradation, it is assumed that diffusion of inflammatory cytokines leads to degradation of the extracellular matrix. When implementing these two mechanisms in a computational in silico modeling workflow, supplemented by in vitro and in vivo experiments, it is shown that biomechanically driven cartilage degradation is concentrated on the damage environment, while inflammation via synovial fluid affects all free cartilage surfaces. It is also proposed how the presented in silico modeling methodology may be used in the future for personalized prognostics and treatment planning of patients with a joint injury.

Predicting the formation of different tissue types during Achilles tendon healing using mechanoregulated and oxygen-regulated frameworks

During Achilles tendon healing in rodents, besides the expected tendon tissue, also cartilage-, bone- and fat-like tissue features have been observed during the first twenty weeks of healing. Several studies have hypothesized that mechanical loading may play a key role in the formation of different tissue types during healing. We recently developed a computational mechanobiological framework to predict tendon tissue production, organization and mechanical properties during tendon healing. In the current study, we aimed to explore possible mechanobiological related mechanisms underlying formation of other tissue types than tendon tissue during tendon healing. To achieve this, we further developed our recent framework to predict formation of different tissue types, based on mechanobiological models established in other fields, which have earlier not been applied to study tendon healing. We explored a wide range of biophysical stimuli, i.e., principal strain, hydrostatic stress, pore pressure, octahedral shear strain, fluid flow, angiogenesis and oxygen concentration, that may promote the formation of different tissue types. The numerical framework predicted spatiotemporal formation of tendon-, cartilage-, bone- and to a lesser degree fat-like tissue throughout the first twenty weeks of healing, similar to recent experimental reports. Specific features of experimental data were captured by different biophysical stimuli. Our modeling approach showed that mechanobiology may play a role in governing the formation of different tissue types that have been experimentally observed during tendon healing. This study provides a numerical tool that can contribute to a better understanding of tendon mechanobiology during healing. Developing these tools can ultimately lead to development of better rehabilitation regimens that stimulate tendon healing and prevent unwanted formation of cartilage-, fat- and bone-like tissues.

Site- and Zone-Dependent Changes in Proteoglycan Content and Biomechanical Properties of Bluntly and Sharply Grooved Equine Articular Cartilage

In this study, we mapped and quantified changes of proteoglycan (PG) content and biomechanical properties in articular cartilage in which either blunt or sharp grooves had been made, both close to the groove and more remote of it, and at the opposing joint surface (kissing site) in equine carpal joints. In nine adult Shetland ponies, standardized blunt and sharp grooves were surgically made in the radiocarpal and middle carpal joints of a randomly chosen front limb. The contralateral control limb was sham-operated. At 39 weeks after surgery, ponies were euthanized. In 10 regions of interest (ROIs) (six remote from the grooves and four directly around the grooves), PG content as a function of tissue-depth and distance-to-groove was estimated using digital densitometry. Biomechanical properties of the cartilage were evaluated in the six ROIs remote from the grooves. Compared to control joints, whole tissue depth PG loss was found in sites adjacent to sharp and, to a larger extent, blunt grooves. Also, superficial PG loss of the surgically untouched kissing cartilage layers was observed. Significant PG loss was observed up to 300 µm (sharp) and at 500 µm (blunt) from the groove into the surrounding tissue. Equilibrium modulus was lower in grooved cartilage than in controls. Grooves, in particular blunt grooves, gave rise to severe PG loss close to the grooved sites and to mild degeneration more remote from the grooves in both sharply and bluntly grooved cartilage and at the kissing sites, resulting in loss of mechanical strength over the 9-month period.

A deep-learning framework for metacarpal-head cartilage-thickness estimation in ultrasound rheumatological images

OBJECTIVE

Rheumatoid arthritis (RA) is a chronic disease characterized by erosive symmetrical polyarthritis. Bone and cartilage are the main joint targets of this disease. Cartilage damage is one of the most relevant determinants of physical disability in RA patients. Cartilage damage is nowadays assessed by clinicians, which manually measure cartilage thickness in ultrasound (US) imaging. This poses issues relevant to intra-and inter-observer variability. Relying on the acquisition of metacarpal-head US images from 38 subjects, this work addresses the problem of automatic cartilage-thickness measurement by designing a new deep-learning (DL) framework.

METHODS

The framework consists of a Convolutional Neural Network (CNN), responsible for regressing cartilage-interface distance fields, followed by a post-processing step to delineate the two cartilage interfaces from the predicted distance fields and compute the cartilage thickness.

RESULTS

Our framework achieved encouraging results with a mean absolute difference (ADF) of 0.032 (±0.026) mm against manual thickness annotation by an expert clinician. When evaluating the intra-observer variability, we obtained an ADF = 0.036 (±0.028) mm.

CONCLUSION

The proposed framework achieved an ADF against manual annotation that was comparable to the intra-observer variability, proving the potential of DL in the field.

SIGNIFICANCE

This work is the first to address the problem of automatic cartilage-thickness estimation in US rheumatological images with DL, paving the way for future research in the field. From a clinical perspective, the proposed framework proved to be a valuable tool to support the clinical routine increasing the reproducibility of cartilage thickness measurements.

Predicting the effect of reduced load level and cell infiltration on spatio-temporal Achilles tendon healing

Mechanobiology plays an important role in tendon healing. However, the relationship between mechanical loading and spatial and temporal evolution of tendon properties during healing is not well understood. This study builds on a recently presented mechanoregulatory computational framework that couples mechanobiological tendon healing to tissue production and collagen orientation. In this study, we investigated how different magnitudes of mechanical stimulation (principal strain) affect the spatio-temporal evolution of tissue production and the temporal evolution of elastic and viscoelastic mechanical parameters. Specifically, we examined the effect of cell infiltration (mimicking migration and proliferation) in the callus on the resulting tissue production by modeling production to depend on local cell density. The model predictions were carefully compared with experimental data from Achilles tendons in rats, at 1, 2 and 4 weeks of healing. In the experiments, the rat tendons had been subjected to free cage activity or reduced load levels through intramuscular botox injections. The simulations that included cell infiltration and strain-regulated collagen production predicted spatio-temporal tissue distributions and mechanical properties similarly to that observed experimentally. In addition, lack of matrix-producing cells in the tendon core during early healing may result in reduced collagen content, regardless of the daily load level. This framework is the first to computationally investigate mechanobiological mechanisms underlying spatial and temporal variations during tendon healing for various magnitudes of loading. This framework will allow further characterization of biomechanical, biological, or mechanobiological processes underlying tendon healing.

An in silico Framework of Cartilage Degeneration That Integrates Fibril Reorientation and Degradation Along With Altered Hydration and Fixed Charge Density Loss

Injurious mechanical loading of articular cartilage and associated lesions compromise the mechanical and structural integrity of joints and contribute to the onset and progression of cartilage degeneration leading to osteoarthritis (OA). Despite extensive in vitro and in vivo research, it remains unclear how the changes in cartilage composition and structure that occur during cartilage degeneration after injury, interact. Recently, in silico techniques provide a unique integrated platform to investigate the causal mechanisms by which the local mechanical environment of injured cartilage drives cartilage degeneration. Here, we introduce a novel integrated Cartilage Adaptive REorientation Degeneration (CARED) algorithm to predict the interaction between degenerative variations in main cartilage constituents, namely collagen fibril disorganization and degradation, proteoglycan (PG) loss, and change in water content. The algorithm iteratively interacts with a finite element (FE) model of a cartilage explant, with and without variable depth to full-thickness defects. In these FE models, intact and injured explants were subjected to normal (2 MPa unconfined compression in 0.1 s) and injurious mechanical loading (4 MPa unconfined compression in 0.1 s). Depending on the mechanical response of the FE model, the collagen fibril orientation and density, PG and water content were iteratively updated. In the CARED model, fixed charge density (FCD) loss and increased water content were related to decrease in PG content. Our model predictions were consistent with earlier experimental studies. In the intact explant model, minimal degenerative changes were observed under normal loading, while the injurious loading caused a reorientation of collagen fibrils toward the direction perpendicular to the surface, intense collagen degradation at the surface, and intense PG loss in the superficial and middle zones. In the injured explant models, normal loading induced intense collagen degradation, collagen reorientation, and PG depletion both on the surface and around the lesion. Our results confirm that the cartilage lesion depth is a crucial parameter affecting tissue degeneration, even under physiological loading conditions. The results suggest that potential fibril reorientation might prevent or slow down fibril degradation under conditions in which the tissue mechanical homeostasis is perturbed like the presence of defects or injurious loading.

Collagen fibres determine the crack morphology in articular cartilage

Cracks in articular cartilage compromise tissue integrity and mechanical properties and lead to chondral lesions if untreated. An understanding of the mechanics of cracked cartilage may help in the prevention of cartilage deterioration and the development of tissue-engineered substitutes. The degeneration of cartilage in the presence of cracks may depend on the ultrastructure and composition of the tissue, which changes with aging, disease and habitual loading. It is unknown if the structural and compositional differences between immature and mature cartilage affect the mechanics of cartilage cracks, possibly predisposing one to a greater risk of degeneration than the other. We used a fibre-reinforced poro-viscoelastic swelling material model that accounts for large deformations and tension-compression non-linearity, and the finite element method to investigate the role of cartilage structure and composition on crack morphology and tissue mechanics. We demonstrate that the crack morphology predicted by our theoretical model agrees well with the histo-morphometric images of young and mature cracked cartilages under indentation loading. We also determined that the crack morphology was primarily dependent on collagen fibre orientation which differs as a function of cartilage depth and tissue maturity. The arcade-like collagen fibre orientation, first discussed by Benninghoff in his classical 1925 paper, appears to be beneficial for slowing the progression of tissue cracks by 'sealing' the crack and partially preserving fluid pressure during loading. Preservation of the natural load distribution between solid and fluid constituents of cartilage may be a key factor in slowing or preventing the propagation of tissue cracks and associated tissue matrix damage. STATEMENT OF SIGNIFICANCE: Cracks in articular cartilage can be detrimental to joint health if not treated, but it is not clear how they propagate and lead to tissue degradation. We used an advanced numerical model to determine the role of cartilage structure and composition on crack morphology under loading. Based on the structure and composition found in immature and mature cartilages, our model successfully predicts the crack morphology in these cartilages and determines that collagen fibre as the major determinant of crack morphology. The arcade-like Benninghoff collagen fibre orientation appears to be crucial in 'sealing' the tissue crack and preserves normal fluid-solid load distribution in cartilage. Inclusion of the arcade-like fibre orientation in tissue-engineered construct may help improve its integration within the host tissue.

Prediction of local fixed charge density loss in cartilage following ACL injury and reconstruction: A computational proof-of-concept study with MRI follow-up

The purpose of this proof-of-concept study was to develop three-dimensional patient-specific mechanobiological knee joint models to simulate alterations in the fixed charged density (FCD) around cartilage lesions during the stance phase of the walking gait. Two patients with anterior cruciate ligament (ACL) reconstructed knees were imaged at 1 and 3 years after surgery. The magnetic resonance imaging (MRI) data were used for segmenting the knee geometries, including the cartilage lesions. Based on these geometries, finite element (FE) models were developed. The gait of the patients was obtained using a motion capture system. Musculoskeletal modeling was utilized to calculate knee joint contact and lower extremity muscle forces for the FE models. Finally, a cartilage adaptation algorithm was implemented in both FE models. In the algorithm, it was assumed that excessive maximum shear and deviatoric strains (calculated as the combination of principal strains), and fluid velocity, are responsible for the FCD loss. Changes in the longitudinal T_1ρ and T₂ relaxation times were postulated to be related to changes in the cartilage composition and were compared with the numerical predictions. In patient 1 model, both the excessive fluid velocity and strain caused the FCD loss primarily near the cartilage lesion. T_1ρ and T₂ relaxation times increased during the follow-up in the same location. In contrast, in patient 2 model, only the excessive fluid velocity led to a slight FCD loss near the lesion, where MRI parameters did not show evidence of alterations. Significance: This novel proof-of-concept study suggests mechanisms through which a local FCD loss might occur near cartilage lesions. In order to obtain statistical evidence for these findings, the method should be investigated with a larger cohort of subjects.

A numerical framework for mechano-regulated tendon healing-Simulation of early regeneration of the Achilles tendon

Mechano-regulation during tendon healing, i.e. the relationship between mechanical stimuli and cellular response, has received more attention recently. However, the basic mechanobiological mechanisms governing tendon healing after a rupture are still not well-understood. Literature has reported spatial and temporal variations in the healing of ruptured tendon tissue. In this study, we explored a computational modeling approach to describe tendon healing. In particular, a novel 3D mechano-regulatory framework was developed to investigate spatio-temporal evolution of collagen content and orientation, and temporal evolution of tendon stiffness during early tendon healing. Based on an extensive literature search, two possible relationships were proposed to connect levels of mechanical stimuli to collagen production. Since literature remains unclear on strain-dependent collagen production at high levels of strain, the two investigated production laws explored the presence or absence of collagen production upon non-physiologically high levels of strain (>15%). Implementation in a finite element framework, pointed to large spatial variations in strain magnitudes within the callus tissue, which resulted in predictions of distinct spatial distributions of collagen over time. The simulations showed that the magnitude of strain was highest in the tendon core along the central axis, and decreased towards the outer periphery. Consequently, decreased levels of collagen production for high levels of tensile strain were shown to accurately predict the experimentally observed delayed collagen production in the tendon core. In addition, our healing framework predicted evolution of collagen orientation towards alignment with the tendon axis and the overall predicted tendon stiffness agreed well with experimental data. In this study, we explored the capability of a numerical model to describe spatial and temporal variations in tendon healing and we identified that understanding mechano-regulated collagen production can play a key role in explaining heterogeneities observed during tendon healing.

Effect of collagen fibril distributions on the crack profile in articular cartilage

BACKGROUND AND OBJECTIVE

Cartilage cracks and fissures may occur due to certain daily life activities such as sports practice, blunt trauma, and matrix fibrillation during early osteoarthritis. These cracks could further grow at the macroscopic level, alter the load distribution pattern in the matrix, limit the joint range of motion, and disturb chondrocytes synthesis. Cracks' shape and deformations in the loaded cartilage may affect the subsequent mechanobiological processes in the long term, likely because of the altered fluid exchange and excessive local deformations in the vicinity of the damage site. The fibrillar structure of the cartilage matrix appeared to have a protective effect against excessive deformations and tissue failure. Hence, in the present study, a fibril reinforced biphasic cartilage model was used to assess the potential role of different fibril orientations on the profile of a vertical crack in cartilage after applying a compressive load.

METHODS

A 20 × 20 × 1.5 mm³ cartilage model was developed with a 0.7 mm length V-shape cut at the center. Using an impermeable indenter, a 27% compression was applied to immature, mature, and isotropic cartilage models. Each of immature and mature groups had 4 different split line directions with respect to the cut edges, including 90°, 45°, 0°, and random orientation. The latter represented the disrupted collagen fibril orientations in early osteoarthritis. The model was verified with the experimental results in the literature.

RESULTS

In the superficial zones, the larger angle between the split lines and cut edges resulted in a wider cut opening. In the absence of collagen fibrils, the isotropic model resulted in a closed edge profile. Also, under a consistently applied compression, the OA model, with random collagen fibril distribution on its surface, had the smallest load-bearing capacity compared to the other models.

CONCLUSIONS

Findings highlighted a primary role of collagen fibrils on the cut profile, which was more pronounced at dynamic rather than static conditions. Split lines perpendicular to the cut edges had some protective effects against the large dislocation of cut edges. These findings could be utilized to develop engineered tissues less susceptible to rupture. Moreover, the outcome of the present study can explain the potential causes of the crack propagation path reported in the literature.

Mechanobiological model for simulation of injured cartilage degradation via pro-inflammatory cytokines and mechanical stimulus

Post-traumatic osteoarthritis (PTOA) is associated with cartilage degradation, ultimately leading to disability and decrease of quality of life. Two key mechanisms have been suggested to occur in PTOA: tissue inflammation and abnormal biomechanical loading. Both mechanisms have been suggested to result in loss of cartilage proteoglycans, the source of tissue fixed charge density (FCD). In order to predict the simultaneous effect of these degrading mechanisms on FCD content, a computational model has been developed. We simulated spatial and temporal changes of FCD content in injured cartilage using a novel finite element model that incorporates (1) diffusion of the pro-inflammatory cytokine interleukin-1 into tissue, and (2) the effect of excessive levels of shear strain near chondral defects during physiologically relevant loading. Cytokine-induced biochemical cartilage explant degradation occurs near the sides, top, and lesion, consistent with the literature. In turn, biomechanically-driven FCD loss is predicted near the lesion, in accordance with experimental findings: regions near lesions showed significantly more FCD depletion compared to regions away from lesions (p<0.01). Combined biochemical and biomechanical degradation is found near the free surfaces and especially near the lesion, and the corresponding bulk FCD loss agrees with experiments. We suggest that the presence of lesions plays a role in cytokine diffusion-driven degradation, and also predisposes cartilage for further biomechanical degradation. Models considering both these cartilage degradation pathways concomitantly are promising in silico tools for predicting disease progression, recognizing lesions at high risk, simulating treatments, and ultimately optimizing treatments to postpone the development of PTOA.

Post-traumatic osteoarthritis is a musculoskeletal disorder where inflammatory processes and abnormal joint loading predispose articular cartilage to degradation after a mechanical injury. Since inflamed and injured cartilage cannot be reversed back to healthy state, prevention of osteoarthritis progression is advisable, a prestigious goal where computational models could serve as tools. The current literature is short of computational models combining both biochemical and biomechanical aspects of osteoarthritis. Thus, here we implemented inflammation of living cartilage tissue followed by biochemical perturbations of tissue homeostasis and shear strain-induced biomechanical degradation in novel cell-to-tissue-level finite element models. The models presented in this paper and enriched by our experimental findings/previous literature provide profound new mechanobiological insights and predictions about cartilage degradation in injured and inflamed tissue under physiologically relevant mechanical loading. We suggest that mechanobiological computational models could be applied as in silico analysis tools that provide clinicians information of the personalized progression of post-traumatic osteoarthritis and decision-making guidance for treatment of the disease.

Clinical Contrast-Enhanced Computed Tomography With Semi-Automatic Segmentation Provides Feasible Input for Computational Models of the Knee Joint

Computational models can provide information on joint function and risk of tissue failure related to progression of osteoarthritis (OA). Currently, the joint geometries utilized in modeling are primarily obtained via manual segmentation, which is time-consuming and hence impractical for direct clinical application. The aim of this study was to evaluate the applicability of a previously developed semi-automatic method for segmenting tibial and femoral cartilage to serve as input geometry for finite element (FE) models. Knee joints from seven volunteers were first imaged using a clinical computed tomography (CT) with contrast enhancement and then segmented with semi-automatic and manual methods. In both segmentations, knee joint models with fibril-reinforced poroviscoelastic (FRPVE) properties were generated and the mechanical responses of articular cartilage were computed during physiologically relevant loading. The mean differences in the absolute values of maximum principal stress, maximum principal strain, and fibril strain between the models generated from semi-automatic and manual segmentations were <1 MPa, <0.72% and <0.40%, respectively. Furthermore, contact areas, contact forces, average pore pressures, and average maximum principal strains were not statistically different between the models (p >0.05). This semi-automatic method speeded up the segmentation process by over 90% and there were only negligible differences in the results provided by the models utilizing either manual or semi-automatic segmentations. Thus, the presented CT imaging-based segmentation method represents a novel tool for application in FE modeling in the clinic when a physician needs to evaluate knee joint function.

Use of Computational Modeling to Study Joint Degeneration: A Review

Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively. Computational modeling of joint degradation allows to estimate the patient-specific progression of OA, which can aid clinicians to estimate the most suitable time window for surgical intervention in osteoarthritic patients. This paper gives an overview of the different approaches used to model different aspects of joint degeneration, thereby focusing mostly on the knee joint. The paper starts by discussing how OA affects the different components of the joint and how these are accounted for in the models. Subsequently, it discusses the different modeling approaches that can be used to answer questions related to OA etiology, progression and treatment. These models are ordered based on their underlying assumptions and technologies: musculoskeletal models, Finite Element models, (gene) regulatory models, multiscale models and data-driven models (artificial intelligence/machine learning). Finally, it is concluded that in the future, efforts should be made to integrate the different modeling techniques into a more robust computational framework that should not only be efficient to predict OA progression but also easily allow a patient’s individualized risk assessment as screening tool for use in clinical practice.

Computational evaluation of altered biomechanics related to articular cartilage lesions observed in vivo

Chondral lesions provide a potential risk factor for development of osteoarthritis. Despite the variety of in vitro studies on lesion degeneration, in vivo studies that evaluate relation between lesion characteristics and the risk for the possible progression of OA are lacking. Here, we aimed to characterize different lesions and quantify biomechanical responses experienced by surrounding cartilage tissue. We generated computational knee joint models with nine chondral injuries based on clinical in vivo arthrographic computed tomography images. Finite element models with fibril-reinforced poro(visco)elastic cartilage and menisci were constructed to simulate physiological loading. Systematically, the lesions experienced increased peak values of maximum principal strain, maximum shear strain, and minimum principal strain in the surrounding chondral tissue (p < 0.01) compared with intact tissue. Depth, volume, and area of the lesion correlated with the maximum shear strain (p < 0.05, Spearman rank correlation coefficient ρ = 0.733-0.917). Depth and volume of the lesion correlated also with the maximum principal strain (p < 0.05, ρ = 0.767, and ρ = 0.717, respectively). However, the lesion area had non-significant correlation with this strain parameter (p = 0.06, ρ = 0.65). Potentially, the introduced approach could be developed for clinical evaluation of biomechanical risks of a chondral lesion and planning an intervention. Statement of Clinical Relevance: In this study, we computationally characterized different in vivo chondral lesions and evaluated their risk of cartilage degeneration. This information is vital in decision-making for intervention in order to prevent post-traumatic osteoarthritis. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Maximum shear strain-based algorithm can predict proteoglycan loss in damaged articular cartilage

Post-traumatic osteoarthritis (PTOA) is a common disease, where the mechanical integrity of articular cartilage is compromised. PTOA can be a result of chondral defects formed due to injurious loading. One of the first changes around defects is proteoglycan depletion. Since there are no methods to restore injured cartilage fully back to its healthy state, preventing the onset and progression of the disease is advisable. However, this is problematic if the disease progression cannot be predicted. Thus, we developed an algorithm to predict proteoglycan loss of injured cartilage by decreasing the fixed charge density (FCD) concentration. We tested several mechanisms based on the local strains or stresses in the tissue for the FCD loss. By choosing the degeneration threshold suggested for inducing chondrocyte apoptosis and cartilage matrix damage, the algorithm driven by the maximum shear strain showed the most substantial FCD losses around the lesion. This is consistent with experimental findings in the literature. We also observed that by using coordinate system-independent strain measures and selecting the degeneration threshold in an ad hoc manner, all the resulting FCD distributions would appear qualitatively similar, i.e., the greatest FCD losses are found at the tissue adjacent to the lesion. The proposed strain-based FCD degeneration algorithm shows a great potential for predicting the progression of PTOA via biomechanical stimuli. This could allow identification of high-risk defects with an increased risk of PTOA progression.

A novel mechanobiological model can predict how physiologically relevant dynamic loading causes proteoglycan loss in mechanically injured articular cartilage

Cartilage provides low-friction properties and plays an essential role in diarthrodial joints. A hydrated ground substance composed mainly of proteoglycans (PGs) and a fibrillar collagen network are the main constituents of cartilage. Unfortunately, traumatic joint loading can destroy this complex structure and produce lesions in tissue, leading later to changes in tissue composition and, ultimately, to post-traumatic osteoarthritis (PTOA). Consequently, the fixed charge density (FCD) of PGs may decrease near the lesion. However, the underlying mechanisms leading to these tissue changes are unknown. Here, knee cartilage disks from bovine calves were injuriously compressed, followed by a physiologically relevant dynamic compression for twelve days. FCD content at different follow-up time points was assessed using digital densitometry. A novel cartilage degeneration model was developed by implementing deviatoric and maximum shear strain, as well as fluid velocity controlled algorithms to simulate the FCD loss as a function of time. Predicted loss of FCD was quite uniform around the cartilage lesions when the degeneration algorithm was driven by the fluid velocity, while the deviatoric and shear strain driven mechanisms exhibited slightly discontinuous FCD loss around cracks. Our degeneration algorithm predictions fitted well with the FCD content measured from the experiments. The developed model could subsequently be applied for prediction of FCD depletion around different cartilage lesions and for suggesting optimal rehabilitation protocols.