The relation between meniscal dynamics and tibiofemoral kinematics.

Over the past 30 years, research on meniscal kinematics has been limited by challenges such as low-resolution imaging and capturing continuous motion from static data. This study aimed to develop a computational knee model that overcomes these limitations and enables the continuous assessment of meniscal dynamics. A high-resolution MRI dataset (n = 11) was acquired in 4 configurations of knee flexion. In each configuration, the menisci were modeled based on the underlying osseous anatomy. Principal Polynomial Shape Analysis (PPSA) was employed for continuous meniscal modeling. Maximal medial anterior horn displacement occurred in 60° of flexion, equaling 6.24 mm posteromedial, while the posterior horn remained relatively stable. At 90° of flexion, the lateral anterior and posterior horn displaced posteromedially, amounting 5.70 mm and 6.51 mm respectively. The maximal observed Average Surface Distance (ASD) equaled 0.70 mm for lateral meniscal modeling in 90° of flexion. Based on our results, a strong relation between meniscal dynamics and tibiofemoral kinematics was confirmed. Expanding on static meniscal modeling and employing PPSA, we derived and validated a standardized and systematic methodological workflow.

Personalized statistical modeling of soft tissue structures in the knee.

Background and Objective: As in vivo measurements of knee joint contact forces remain challenging, computational musculoskeletal modeling has been popularized as an encouraging solution for non-invasive estimation of joint mechanical loading. Computational musculoskeletal modeling typically relies on laborious manual segmentation as it requires reliable osseous and soft tissue geometry. To improve on feasibility and accuracy of patient-specific geometry predictions, a generic computational approach that can easily be scaled, morphed and fitted to patient-specific knee joint anatomy is presented. Methods: A personalized prediction algorithm was established to derive soft tissue geometry of the knee, originating solely from skeletal anatomy. Based on a MRI dataset (n = 53), manual identification of soft-tissue anatomy and landmarks served as input for our model by use of geometric morphometrics. Topographic distance maps were generated for cartilage thickness predictions. Meniscal modeling relied on wrapping a triangular geometry with varying height and width from the anterior to the posterior root. Elastic mesh wrapping was applied for ligamentous and patellar tendon path modeling. Leave-one-out validation experiments were conducted for accuracy assessment. Results: The Root Mean Square Error (RMSE) for the cartilage layers of the medial tibial plateau, the lateral tibial plateau, the femur and the patella equaled respectively 0.32 mm (range 0.14-0.48), 0.35 mm (range 0.16-0.53), 0.39 mm (range 0.15-0.80) and 0.75 mm (range 0.16-1.11). Similarly, the RMSE equaled respectively 1.16 mm (range 0.99-1.59), 0.91 mm (0.75-1.33), 2.93 mm (range 1.85-4.66) and 2.04 mm (1.88-3.29), calculated over the course of the anterior cruciate ligament, posterior cruciate ligament, the medial and the lateral meniscus. Conclusion: A methodological workflow is presented for patient-specific, morphological knee joint modeling that avoids laborious segmentation. By allowing to accurately predict personalized geometry this method has the potential for generating large (virtual) sample sizes applicable for biomechanical research and improving personalized, computer-assisted medicine.

Separating positional noise from neutral alignment in multicomponent statistical shape models.

Given sufficient training samples, statistical shape models can provide detailed population representations for use in anthropological and computational genetic studies, injury biomechanics, musculoskeletal disease models or implant design optimization. While the technique has become extremely popular for the description of isolated anatomical structures, it suffers from positional interference when applied to coupled or articulated input data. In the present manuscript we describe and validate a novel approach to extract positional noise from such coupled data. The technique was first validated and then implemented in a multicomponent model of the lower limb. The impact of noise on the model itself as well as on the description of sexual dimorphism was evaluated. The novelty of our methodology lies in the fact that no rigid transformations are calculated or imposed on the data by means of idealized joint definitions and by extension the models obtained from them.

Statistical Shape Modeling of Skeletal Anatomy for Sex Discrimination: Their Training Size, Sexual Dimorphism, and Asymmetry.

Purpose: Statistical shape modeling provides a powerful tool for describing and analyzing human anatomy. By linearly combining the variance of the shape of a population of a given anatomical entity, statistical shape models (SSMs) identify its main modes of variation and may approximate the total variance of that population to a selected threshold, while reducing its dimensionality. Even though SSMs have been used for over two decades, they lack in characterization of their goodness of prediction, in particular when defining whether these models are actually representative for a given population. Methods: The current paper presents, to the authors' knowledge, the most extent lower limb anatomy shape model considering the pelvis, femur, patella, tibia, fibula, talus, and calcaneum to date. The present study includes the segmented training shapes (n = 542) obtained from 271 lower limb CT scans. The different models were evaluated in terms of accuracy, compactness, generalizability as well as specificity. Results: The size of training samples needed in each model so that it can be considered population covering was estimated to approximate around 200 samples, based on the generalizability properties of the different models. Simultaneously differences in gender and patterns in left-right asymmetry were identified and characterized. Size was found to be the most pronounced sexual discriminator whereas intra-individual variations in asymmetry were most pronounced at the insertion site of muscles. Conclusion: For models aimed at population covering descriptive studies, the number of training samples required should amount a sizeable 200 samples. The geometric morphometric method for sex discrimination scored excellent, however, it did not largely outperformed traditional methods based on discrete measures.

A discrete element model to predict anatomy of the psoas muscle and path of the tendon: Design implications for total hip arthroplasty.

BACKGROUND: The accurate estimation of a muscle's line of action is a fundamental requirement in computational modelling. We present a novel anatomical muscle wrapping technique and demonstrate its clinical use on the evaluation of the Psoas muscle mechanics in hip arthroplasty. METHODS: A volume preserving, spring model to parameterize muscle anatomy changes during motion is presented. Validation was performed by a CT scan of a cadaver model in multiple positions. The predicted psoas musculotendinous path was compared with the actual imaging findings. In a second stage, psoas kinetics were compared between a conventional versus a resurfacing hip arthroplasty during gait. FINDINGS: Anatomy prediction error was found to be 2.12 mm on average (SD 1.34 mm). When applied to psoas mechanics during walking, the muscle was found to wrap predominantly around the femoral head providing a biomechanically efficient and nearly constant moment arm for flexion during the entire gait cycle. However, this advantage was found to be lost in small diameter hip arthroplasty designs resulting in an important mechanical disadvantage. The moment arm for flexion, was on average 36% (SD 0.03%) lower in the small diameter conventional hip arthroplasty as compared to the large diameter head of the hip resurfacing and this difference was highly significant. (p < 0.001). INTERPRETATION: Despite the shortcomings of an "in silico" and cadaveric study, our findings are in accordance with previous clinical and gait studies. Furthermore, the findings are strongly in favour of large diameter implant designs, warranting their further development and optimisation.

Optimized robotic setup for automated active thermography using advanced path planning and visibility study.

In-line inspection of advanced components remains a challenging task in industry. The authors will describe an automated methodology that uses numerical simulations to automatically determine the best set of experimental parameters to inspect the structure on defects using active thermography. The inspection is performed using a robotic arm and advanced path-planning tools to determine the optimal positions of the measurement points and excitation points. During the path planning, the directional emissivity is considered for the complex surface, and a minimization of the amount of measurement points is performed. The numerical simulation optimization used a genetic algorithm and spline regression model to optimize the heat power, robot speed, camera frame rate, and excitation timing to fulfill the automatic inspection.

Computer-based estimation of the hip joint reaction force and hip flexion angle in three different sitting configurations.

Sitting is part of our daily work and leisure activities and can be performed in different configurations. To date, the impact of different sitting configurations on hip joint loading has not been studied. We therefore evaluated the hip joint reaction force (HJRF) and hip flexion angle in a virtual representative male Caucasian population by means of musculoskeletal modelling of three distinct sitting configurations: a simple chair, a car seat and a kneeling chair configuration. The observed median HJRF in relation to body weight and hip flexion angle, respectively, was 22.3% body weight (%BW) and 63° for the simple chair, 22.5%BW and 79° for the car seat and 8.7%BW and 50° for the kneeling chair. Even though the absolute values of HJRF are low compared to the forces generated during dynamic activities, a relative reduction of over 50% in HJRF was observed in the kneeling chair configuration. Second, the hip flexion angles were both in the kneeling chair (-29°) and simple chair configuration (-16°) lower compared to the car seat and, as such, did not reach the threshold value for femoroacetabular conflict. In conclusion, the kneeling chair appears to hold the greatest potential as an ergonomic sitting configuration for the hip joint.