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We present a dataset comprising motion capture, inertial measurement unit data, and sagittal-plane video data from walking at three different instructed speeds (slow, comfortable, fast). The dataset contains 51 healthy participants with approximately 60 walking trials from each participant. Each walking trial contains data from motion capture, inertial measurement units, and computer vision. Motion capture data comprises ground reaction forces and moments from floor-embedded force plates and the 3D trajectories of subject-worn motion capture markers. Inertial measurement unit data comprises 3D accelerometer readings and 3D orientations from the lower limbs and pelvis. Computer vision data comprises 2D keypoint trajectories detected using the OpenPose human pose estimation algorithm from sagittal-plane video of the walking trial. Additionally, the dataset contains participant demographic and anthropometric information such as mass, height, sex, age, lower limb dimensions, and knee intercondylar distance measured from magnetic resonance images. The dataset can be used in musculoskeletal modelling and simulation to calculate kinematics and kinetics of motion and to compare data between motion capture, inertial measurement, and video capture.

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: 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.

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PURPOSE: Estimating loading of the knee joint may be helpful in managing degenerative joint diseases. Contemporary methods to estimate loading involve calculating knee joint contact forces using musculoskeletal modeling and simulation from motion capture (MOCAP) data, which must be collected in a specialized environment and analyzed by a trained expert. To make the estimation of knee joint loading more accessible, simple input predictors should be used for predicting knee joint loading using artificial neural networks. METHODS: We trained feedforward artificial neural networks (ANNs) to predict knee joint loading peaks from the mass, height, age, sex, walking speed, and knee flexion angle (KFA) of subjects using their existing MOCAP data. We also collected an independent MOCAP dataset while recording walking with a video camera (VC) and inertial measurement units (IMUs). We quantified the prediction accuracy of the ANNs using walking speed and KFA estimates from (1) MOCAP data, (2) VC data, and (3) IMU data separately (i.e., we quantified three sets of prediction accuracy metrics). RESULTS: Using portable modalities, we achieved prediction accuracies between 0.13 and 0.37 root mean square error normalized to the mean of the musculoskeletal analysis-based reference values. The correlation between the predicted and reference loading peaks varied between 0.65 and 0.91. This was comparable to the prediction accuracies obtained when obtaining predictors from motion capture data. DISCUSSION: The prediction results show that both VCs and IMUs can be used to estimate predictors that can be used in estimating knee joint loading outside the motion laboratory. Future studies should investigate the usability of the methods in an out-of-laboratory setting.

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PURPOSE: Anterior cruciate ligament (ACL) rupture is increasingly common in paediatric and adolescent populations, typically requiring surgical ACL reconstruction (ACLR) to restore knee stability. However, ACLR substantially alters knee biomechanics (e.g., motion and tissue mechanics) placing the patient at elevated risk of early-onset knee osteoarthritis. METHODS: This study employed a linked neuromusculoskeletal (NMSK)-finite element (FE) model to determine effects of four critical ACLR surgical parameters (graft type, size, location and pre-tension) on tibial articular cartilage stresses in three paediatric knees of different sizes during walking. Optimal surgical combinations were defined by minimal kinematic and tibial cartilage stress deviations in comparison to a corresponding intact healthy knee, with substantial deviations defined by normalized root mean square error (nRMSE) > 10%. RESULTS: Results showed unique trends of principal stress deviations across knee sizes with small knee showing least deviation from intact knee, followed by large- and medium-sized knees. The nRMSE values for cartilage stresses displayed notable variability across different knees. Surgical combination yielding the highest nRMSE in comparison to the one with lowest nRMSE resulted in an increase of maximum principal stress on the medial tibial cartilage by 18.0%, 6.0% and 1.2% for small, medium and large knees, respectively. Similarly, there was an increase of maximum principal stress on lateral tibial cartilage by 11.2%, 4.1% and 12.7% for small, medium and large knees, respectively. Knee phenotype and NMSK factors contributed to deviations in knee kinematics and tibial cartilage stresses. Although optimal surgical configurations were found for each knee size, no generalizable trends emerged emphasizing the subject-specific nature of the knee and neuromuscular system. CONCLUSION: Study findings underscore subject-specific complexities in ACLR biomechanics, necessitating personalized surgical planning for effective restoration of native motion and tissue mechanics. Future research should expand investigations to include a broader spectrum of subject-specific factors to advance personalized surgical planning. LEVEL OF EVIDENCE: Level III.

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Obesity is a known risk factor for development of osteoarthritis (OA). Numerical tools like finite-element (FE) models combined with degenerative algorithms have been developed to understand the interplay between OA and obesity. In this study, we aimed to predict knee cartilage degeneration in a cohort of obese adults to investigate the importance of patient-specific information on degeneration predictions. We used a validated FE modeling approach and three different age-dependent functions (step-wise, exponential, and linear) to simulate cartilage degradation under overloading in the knee joint. Gait motion analysis and magnetic resonance imaging data from 115 obese individuals with knee OA were used for musculoskeletal and FE modeling. Cartilage degeneration predictions were contrasted with Kellgren-Lawrence (KL) and Boston-Leeds Osteoarthritis Knee Score (BLOKS) grades. The findings show that overall, the similarities between numerical predictions and clinical measures were better for the medial (average area under the curve (AUC) = 0.62) compared to the lateral compartment (average AUC = 0.52) of the knee. Classification results for KL grades, full patient-specific models and patient-specific geometry with generic gait data showed higher AUC values (AUC = 0.71 and AUC = 0.68, respectively) compared to generic geometry and patient-specific gait (AUC = 0.48). For BLOKS grades, AUC values for both full patient-specific models and for patient-specific geometry with generic gait locomotion were higher (AUC = 0.66 and AUC = 0.64, respectively) compared to when the generic geometry and patient-specific gait were used (AUC = 0.53). In summary, our study highlights the importance of considering individual information in knee OA prediction. Nevertheless, our findings suggest that personalized gait play a smaller role in the OA prediction and classification capacity than personalized joint geometry.

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Currently, there are no methods or tools available in clinical practice for classifying future knee osteoarthritis (KOA). In this study, we aimed to fill this gap by classifying future KOA into three severity grades: KL01 (healthy), KL2 (moderate), and KL34 (severe) based on the Kellgren-Lawrance scale. Due to the complex nature of multiclass classification, we used a two-stage method, which separates the classification task into two binary classifications (KL01 vs. KL234 in the first stage and KL2 vs. KL34 in the second stage). Our machine learning (ML) model used two Balanced Random Forest algorithms and was trained with gender, age, height, weight, and quantitative knee morphology obtained from magnetic resonance imaging. Our training dataset comprised longitudinal 8-year follow-up data of 1213 knees from the Osteoarthritis Initiative. Through extensive experimentation with various feature combinations, we identified KL baseline and weight as the most essential features, while gender surprisingly proved to be one of the least influential feature. Our best classification model generated a weighted F1 score of 79.0% and a balanced accuracy of 65.9%. The area under the receiver operating characteristic curve was 83.0% for healthy (KL01) versus moderate (KL2) or severe (KL34) KOA patients and 86.6% for moderate (KL2) versus severe (KL34) KOA patients. We found a statistically significant difference in performance between our two-stage classification model and the traditional single-stage classification model. These findings demonstrate the encouraging results of our two-stage classification model for multiclass KOA severity classification, suggesting its potential application in clinical settings in future.

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The ability of articular cartilage to withstand significant mechanical stresses during activities, such as walking or running, relies on its distinctive structure. Integrating detailed tissue properties into subject-specific biomechanical models is challenging due to the complexity of analyzing these characteristics. This limitation compromises the accuracy of models in replicating cartilage function and impacts predictive capabilities. To address this, methods revealing cartilage function at the constituent-specific level are essential. In this study, we demonstrated that computational modeling derived individual constituent-specific biomechanical properties could be predicted by a novel nanoparticle contrast-enhanced computer tomography (CECT) method. We imaged articular cartilage samples collected from the equine stifle joint (n = 60) using contrast-enhanced micro-computed tomography (µCECT) to determine contrast agents' intake within the samples, and compared those to cartilage functional properties, derived from a fibril-reinforced poroelastic finite element model. Two distinct imaging techniques were investigated: conventional energy-integrating µCECT employing a cationic tantalum oxide nanoparticle (Ta2O5-cNP) contrast agent and novel photon-counting µCECT utilizing a dual-contrast agent, comprising Ta2O5-cNP and neutral iodixanol. The results demonstrate the capacity to evaluate fibrillar and non-fibrillar functionality of cartilage, along with permeability-affected fluid flow in cartilage. This finding indicates the feasibility of incorporating these specific functional properties into biomechanical computational models, holding potential for personalized approaches to cartilage diagnostics and treatment.

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Physics-based modeling methods have the potential to investigate the mechanical factors associated with knee osteoarthritis (OA) and predict the future radiographic condition of the joint. However, it remains unclear what level of detail is optimal in these methods to achieve accurate prediction results in cohort studies. In this work, we extended a template-based finite element (FE) method to include the lateral and medial compartments of the tibiofemoral joint and simulated the mechanical responses of 97 knees under three conditions of gait loading. Furthermore, the effects of variations in cartilage thickness and failure equation on predicted cartilage degeneration were investigated. Our results showed that using neural network-based estimations of peak knee loading provided classification performances of 0.70 (AUC, p < 0.05) in distinguishing between knees that developed severe OA or mild OA and knees that did not develop OA eight years after a healthy radiographic baseline. However, FE models incorporating subject-specific femoral and tibial cartilage thickness did not improve this classification performance, suggesting there exists an optimal point between personalized loading and geometry for discrimination purposes. In summary, we proposed a modeling framework that streamlines the rapid generation of individualized knee models achieving promising classification performance while avoiding motion capture and cartilage image segmentation.

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In order to improve the ability of clinical diagnosis to differentiate articular cartilage (AC) injury of different origins, this study explores the sensitivity of mid-infrared (MIR) spectroscopy for detecting structural, compositional, and functional changes in AC resulting from two injury types. Three grooves (two in parallel in the palmar-dorsal direction and one in the mediolateral direction) were made via arthrotomy in the AC of the radial facet of the third carpal bone (middle carpal joint) and of the intermediate carpal bone (the radiocarpal joint) of nine healthy adult female Shetland ponies (age = 6.8 ± 2.6 years; range 4-13 years) using blunt and sharp tools. The defects were randomly assigned to each of the two joints. Ponies underwent a 3-week box rest followed by 8 weeks of treadmill training and 26 weeks of free pasture exercise before being euthanized for osteochondral sample collection. The osteochondral samples underwent biomechanical indentation testing, followed by MIR spectroscopic assessment. Digital densitometry was conducted afterward to estimate the tissue's proteoglycan (PG) content. Subsequently, machine learning models were developed to classify the samples to estimate their biomechanical properties and PG content based on the MIR spectra according to injury type. Results show that MIR is able to discriminate healthy from injured AC (91%) and between injury types (88%). The method can also estimate AC properties with relatively low error (thickness = 12.7% mm, equilibrium modulus = 10.7% MPa, instantaneous modulus = 11.8% MPa). These findings demonstrate the potential of MIR spectroscopy as a tool for assessment of AC integrity changes that result from injury.

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Knee joint kinematics and kinetics analyzed by musculoskeletal (MS) modeling are often utilized in finite element (FE) models, estimating tissue-level mechanical responses. We compared knee cartilage stresses, strains, and centers of pressure of FE models driven by two widely used MS models, implemented in AnyBody and OpenSim. Minor discrepancies in the results were observed between the models. AnyBody-driven FE models showed slightly higher stresses in the medial tibial cartilage, while OpenSim-driven FE models estimated more anterior and lateral center of pressure. Recognizing these differences in the MS-FE models is important to ensure reliable analysis of cartilage mechanics and failure and simulation of rehabilitation.

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Articular cartilage exhibits site-specific biomechanical properties. However, no study has comprehensively characterized site-specific cartilage properties from the same knee joints at different stages of osteoarthritis (OA). Cylindrical osteochondral explants (n = 381) were harvested from donor-matched lateral and medial tibia, lateral and medial femur, patella, and trochlea of cadaveric knees (N = 17). Indentation test was used to measure the elastic and viscoelastic mechanical properties of the samples, and Osteoarthritis Research Society International (OARSI) grading system was used to categorize the samples into normal (OARSI 0-1), early OA (OARSI 2-3), and advanced OA (OARSI 4-5) groups. OA-related changes in cartilage mechanical properties were site-specific. In the lateral and medial tibia and trochlea sites, equilibrium, instantaneous and dynamic moduli were higher (p < 0.001) in normal tissue than in early and advanced OA tissue. In lateral and medial femur, equilibrium, instantaneous and dynamic moduli were smaller in advanced OA, but not in early OA, than in normal tissue. The phase difference (0.1-0.25 Hz) between stress and strain was significantly smaller (p < 0.05) in advanced OA than in normal tissue across all sites except medial tibia. Our results indicated that in contrast to femoral and patellar cartilage, equilibrium, instantaneous and dynamic moduli of the tibia and trochlear cartilage decreased in early OA. These may suggest that the tibia and trochlear cartilage degrades faster than the femoral and patellar cartilage. The information is relevant for developing site-specific computational models and engineered cartilage constructs.

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BACKGROUND AND OBJECTIVE: Incidence of paediatric anterior cruciate ligament (ACL) rupture has increased substantially over recent decades. Following ACL rupture, ACL reconstruction (ACLR) surgery is typically performed to restore passive knee stability. This surgery involves replacing the failed ACL with a graft, however, surgeons must select from range of surgical parameters (e.g., type, size, insertion, and pre-tension) with no robust evidence guiding these decisions. This study presents a systemmatic computational approach to study effects of surgical parameter variation on kinematics of paediatric knees. METHODS: This study used sequentially-linked neuromusculoskeletal (NMSK) finite element (FE) models of three paediatric knees to estimate the: (i) sensitivity of post-operative knee kinematics to four surgical parameters (type, size, insertion, and pre-tension) through multi-input multi-output sensitivity analysis; (ii) influence of motion and loading conditions throughout stance phase of walking gait on sensitivity indices; and (iii) influence of subject-specific anatomy (i.e., knee size) on sensitivivty indices. A previously validated FE model of the intact knee for each subject served as a reference against which ACLR knee kinematics were compared. RESULTS: Sensitivity analyses revealed significant influences of surgical parameters on ACLR knee kinematics, albeit without discernible trend favouring any one parameter. Graft size and pre-tension were primary drivers of variation in knee translations and rotations, however, their effects fluctuated across stance indicating motion and loading conditions affect system sensitivity to surgical parameters. Importantly, the sensitivity of knee kinematics to surgical parameter varied across subjects, indicating geometry (i.e., knee size) influenced system sensitivity. Notably, alterations in graft parameters yielded substantial effects on kinematics (normalized root-mean-square-error > 10 %) compared to intact knee models, indicating surgical parameters vary post-operative knee kinematics. CONCLUSIONS: Overall, this initial study highlights the importance of surgical parameter selection on post-operative kinematics in the paediatric ACLR knee, and provides evidence of the need for personalized surgical planning to ultimately enhance patient outcomes.

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Osteoarthritis (OA) is a multifactorial joint disease characterized by articular cartilage degradation. Risk factors for OA include joint trauma, obesity, and inflammation, each of which can affect joint health independently, but their interaction and the associated consequences of such interaction were largely unexplored. Here, we studied compositional and structural alterations in knee joint cartilages of Sprague-Dawley rats exposed to two OA risk factors: joint injury and diet-induced obesity. Joint injury was imposed by surgical transection of anterior cruciate ligaments (ACLx), and obesity was induced by a high fat/high sucrose diet. Depth-dependent proteoglycan (PG) content and collagen structural network of cartilage were measured from histological sections collected previously in Collins et al.. (2015). We found that ACLx primarily affected the superficial cartilages. Compositionally, ACLx led to reduced PG content in lean animals, but increased PG content in obese rats. Structurally, ACLx caused disorganization of collagenous network in both lean and obese animals through increased collagen orientation in the superficial tissues and a change in the degree of fibrous alignment. However, the cartilage degradation attributed to joint injury and obesity was not necessarily additive when the two risk factors were present simultaneously, particularly for PG content and collagen orientation in the superficial tissues. Interestingly, sham surgeries caused a through-thickness disorganization of collagen network in lean and obese animals. We conclude that the interactions of multiple OA risk factors are complex and their combined effects cannot be understood by superposition principle. Further research is required to elucidate the interactive mechanism between OA subtypes.

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The distal semitendinosus tendon is commonly harvested for anterior cruciate ligament reconstruction, inducing substantial morbidity at the knee. The aim of this study was to probe how morphological changes of the semitendinosus muscle after harvest of its distal tendon for anterior cruciate ligament reconstruction affects knee flexion strength and whether the knee flexor synergists can compensate for the knee flexion weakness. Ten participants 8-18 months after anterior cruciate ligament reconstruction with an ipsilateral distal semitendinosus tendon autograft performed isometric knee flexion strength testing (15°, 45°, 60°, and 90°; 0° = knee extension) positioned prone on an isokinetic dynamometer. Morphological parameters extracted from magnetic resonance images were used to inform a musculoskeletal model. Knee flexion moments estimated by the model were then compared with those measured experimentally at each knee angle position. A statistically significant between-leg difference in experimentally-measured maximal isometric strength was found at 60° and 90°, but not 15° or 45°, of knee flexion. The musculoskeletal model matched the between-leg differences observed in experimental knee flexion moments at 15° and 45° but did not well estimate between-leg differences with a more flexed knee, particularly at 90°. Further, the knee flexor synergists could not physiologically compensate for weakness in deep knee flexion. These results suggest additional factors other than knee flexor muscle morphology play a role in knee flexion weakness following anterior cruciate ligament reconstruction with a distal semitendinosus tendon graft and thus more work at neural and microscopic levels is required for informing treatment and rehabilitation in this demographic.

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In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X-ray and magnetic resonance imaging (MRI) data. For the X-ray analysis, we developed a deep learning (DL) based model to segment femur and tibia. In case of MRIs, we utilized previously validated segmentations of femur, tibia, corresponding cartilage tissues, and menisci. Osteophyte detection was performed using DL models in four compartments: medial femur (FM), lateral femur (FL), medial tibia (TM), and lateral tibia (TL). To analyze the confounding effects of soft tissues, we investigated their morphology in combination with bones, including bones+cartilage, bones+menisci, and all the tissues. From X-ray-based 2D morphology, the models yielded balanced accuracy of 0.73, 0.69, 0.74, and 0.74 for FM, FL, TM, TL, respectively. Using 3D bone morphology from MRI, balanced accuracy was 0.80, 0.77, 0.71, and 0.76, respectively. The performance was higher than in 2D for all the compartments except for TM, with significant improvements observed for femoral compartments. Adding menisci or cartilage morphology consistently improved balanced accuracy in TM, with the greatest improvement seen for small osteophyte. Otherwise, the models performed similarly to bones-only. Our experiments demonstrated that MRI-based models show higher detection capability than X-ray based models for identifying knee osteophytes. This study highlighted the feasibility of automated osteophyte detection from X-ray and MRI data and suggested further need for development of osteophyte assessment criteria in addition to OARSI, particularly, for early osteophytic changes.

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PURPOSE: Clinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size and cannot quantify the tissue microstructure. We demonstrate a robust deep-learning method for enhancing clinical CT images, only requiring a limited set of easy-to-acquire training data. METHODS: Knee tissue from five cadavers and six total knee replacement patients, and 14 teeth from eight patients were scanned using laboratory CT as training data for the developed super-resolution (SR) technique. The method was benchmarked against ex vivo test set, 52 osteochondral samples are imaged with clinical and laboratory CT. A quality assurance phantom was imaged with clinical CT to quantify the technical image quality. To visually assess the clinical image quality, musculoskeletal and maxillofacial CBCT studies were enhanced with SR and contrasted to interpolated images. A dental radiologist and surgeon reviewed the maxillofacial images. RESULTS: The SR models predicted the bone morphological parameters on the ex vivo test set more accurately than conventional image processing. The phantom analysis confirmed higher spatial resolution on the SR images than interpolation, but image grayscales were modified. Musculoskeletal and maxillofacial CBCT images showed more details on SR than interpolation; however, artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores for both SR and interpolation. The source code and pretrained networks are publicly available. CONCLUSION: Model training with laboratory modalities could push the resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT. A larger maxillofacial training dataset is recommended for dental applications.

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Gait modification is a common nonsurgical approach to alter the mediolateral distribution of knee contact forces, intending to decelerate or postpone the progression of mechanically induced knee osteoarthritis (KOA). Nevertheless, the success rate of these approaches is controversial, with no studies conducted to assess alterations in tissue-level knee mechanics governing cartilage degradation response in KOA patients undertaking gait modifications. Thus, here we investigated the effect of different conventional gait conditions and modifications on tissue-level knee mechanics previously suggested as indicators of collagen network damage, cell death, and loss of proteoglycans in knee cartilage. Five participants with medial KOA were recruited and musculoskeletal finite element analyses were conducted to estimate subject-specific tissue mechanics of knee cartilages during two gait conditions (i.e., barefoot and shod) and six gait modifications (i.e., 0°, 5°, and 10° lateral wedge insoles, toe-in, toe-out, and wide stance). Based on our results, the optimal gait modification varied across the participants. Overall, toe-in, toe-out, and wide stance showed the greatest reduction in tissue mechanics within medial tibial and femoral cartilages. Gait modifications could effectually alter maximum principal stress (~20 ± 7%) and shear strain (~9 ± 4%) within the medial tibial cartilage. Nevertheless, lateral wedge insoles did not reduce joint- and tissue-level mechanics considerably. Significance: This proof-of-concept study emphasizes the importance of the personalized design of gait modifications to account for biomechanical risk factors associated with cartilage degradation.

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Cartilage and synovial fluid are challenging to observe separately in native computed tomography (CT). We report the use of triple contrast agent (bismuth nanoparticles [BiNPs], CA4+, and gadoteridol) to image and segment cartilage in cadaveric knee joints with a clinical CT scanner. We hypothesize that BiNPs will remain in synovial fluid while the CA4+ and gadoteridol will diffuse into cartilage, allowing (1) segmentation of cartilage, and (2) evaluation of cartilage biomechanical properties based on contrast agent concentrations. To investigate these hypotheses, triple contrast agent was injected into both knee joints of a cadaver (N = 1), imaged with a clinical CT at multiple timepoints during the contrast agent diffusion. Knee joints were extracted, imaged with micro-CT (µCT), and biomechanical properties of the cartilage surface were determined by stress-relaxation mapping. Cartilage was segmented and contrast agent concentrations (CA4+ and gadoteridol) were compared with the biomechanical properties at multiple locations (n = 185). Spearman's correlation between cartilage thickness from clinical CT and reference µCT images verifies successful and reliable segmentation. CA4+ concentration is significantly higher in femoral than in tibial cartilage at 60 min and further timepoints, which corresponds to the higher Young's modulus observed in femoral cartilage. In this pilot study, we show that (1) large BiNPs do not diffuse into cartilage, facilitating straightforward segmentation of human knee joint cartilage in a clinical setting, and (2) CA4+ concentration in cartilage reflects the biomechanical differences between femoral and tibial cartilage. Thus, the triple contrast agent CT shows potential in cartilage morphology and condition estimation in clinical CT.

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Tissue swelling represents an early sign of osteoarthritis, reflecting osmolarity changes from iso- to hypo-osmotic in the diseased joints. Increased tissue hydration may drive cell swelling. The opposing cartilages in a joint may swell differently, thereby predisposing the more swollen cartilage and cells to mechanical injuries. However, our understanding of the tissue-cell interdependence in osmotically loaded joints is limited as tissue and cell swellings have been studied separately. Here, we measured tissue and cell responses of opposing patellar (PAT) and femoral groove (FG) cartilages in lapine knees exposed to an extreme hypo-osmotic challenge. We found that the tissue matrix and most cells swelled during the hypo-osmotic challenge, but to a different extent (tissue: <3%, cells: 11%-15%). Swelling-induced tissue strains were anisotropic, showing 2%-4% stretch and 1%-2% compression along the first and third principal directions, respectively. These strains were amplified by 5-8 times in the cells. Interestingly, the first principal strains of tissue and cells occurred in different directions (60-61° for tissue vs. 8-13° for cells), suggesting different mechanisms causing volume expansion in the tissue and the cells. Instead of the continuous swelling observed in the tissue matrix, >88% of cells underwent regulatory volume decrease to return to their pre-osmotic challenge volumes. Cell shapes changed in the early phase of swelling but stayed constant thereafter. Kinematic changes to tissue and cells were larger for PAT cartilage than for FG cartilage. We conclude that the swelling-induced deformation of tissue and cells is anisotropic. Cells actively restored volume independent of the surrounding tissues and seemed to prioritize volume restoration over shape restoration. Our findings shed light on tissue-cell interdependence in changing osmotic environments that is crucial for cell mechano-transduction in swollen/diseased tissues.

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Finite element (FE) models have been widely used to investigate knee joint biomechanics. Most of these models have been developed to study adult knees, neglecting pediatric populations. In this study, an atlas-based approach was employed to develop subject-specific FE models of the knee for eight typically developing pediatric individuals. Initially, validation simulations were performed at four passive tibiofemoral joint (TFJ) flexion angles, and the resulting TFJ and patellofemoral joint (PFJ) kinematics were compared to corresponding patient-matched measurements derived from magnetic resonance imaging (MRI). A neuromusculoskeletal-(NMSK)-FE pipeline was then used to simulate knee biomechanics during stance phase of walking gait for each participant to evaluate model simulation of a common motor task. Validation simulations demonstrated minimal error and strong correlations between FE-predicted and MRI-measured TFJ and PFJ kinematics (ensemble average of root mean square errors < 5 mm for translations and < 4.1° for rotations). The FE-predicted kinematics were strongly correlated with published reports (ensemble average of Pearson's correlation coefficients (ρ) > 0.9 for translations and ρ > 0.8 for rotations), except for TFJ mediolateral translation and abduction/adduction rotation. For walking gait, NMSK-FE model-predicted knee kinematics, contact areas, and contact pressures were consistent with experimental reports from literature. The strong agreement between model predictions and experimental reports underscores the capability of sequentially linked NMSK-FE models to accurately predict pediatric knee kinematics, as well as complex contact pressure distributions across the TFJ articulations. These models hold promise as effective tools for parametric analyses, population-based clinical studies, and enhancing our understanding of various pediatric knee injury mechanisms. They also support intervention design and prediction of surgical outcomes in pediatric populations.

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