Inverse remodelling algorithm identifies habitual manual activities of primates based on metacarpal bone architecture

Predicting physiological hip joint loads with inverse bone remodeling using clinically available QCT images

BACKGROUND AND OBJECTIVE

Assessing joint-level loading conditions in vivo is challenging due to invasive measurement or complex computation. Inverse bone remodeling (IBR) offers a different approach by recovering the loading conditions directly from computed tomography (CT) images of the bone microstructure by finding the magnitudes to a set of load cases that load the bone optimally, i.e., maximally homogeneously. An efficient IBR method was recently proposed based on homogenized finite element (hFE) models. This study compared the hip joint load predictions of hFE-based IBR with clinically feasible CT scans to those obtained with the current gold standard, micro-FE-based IBR.

METHODS

A set of 20 proximal femora was scanned ex vivo, both with a clinical quantitative CT (QCT) scanner (0.3 mm resolution) and an Xtreme CT II (XCT2) scanner (0.03 mm resolution). Finite element (FE) models with decreasing complexity were automatically created from those images. Micro-FE (µFE) models based on XCT2 images served as a baseline. hFE models based on the QCT images were created as clinically feasible models. Further intermediate models were created to trace sources of errors. IBR was applied to predict the optimal scaling factors of twelve unit load cases distributed over the femoral head.

RESULTS

The predicted loads of the newly developed workflow for QCT images within IBR followed a trend seen previously with hFE models created from high-resolution images, such as XCT2. The peak load magnitudes of µFE and hFE-based IBR were well correlated (R²=76.8 %), and the overall distribution of the loads was similar. However, an additional peak load calibration was required to obtain quantitative agreement (CCC=82.8 %).

CONCLUSIONS

A thorough comparison of µFE-based IBR and hFE-based IBR using QCT data was performed for the first time. A clinically feasible workflow, including a peak calibration, is presented, allowing for fast prediction of physiological peak hip joint loads.

Hip joint load prediction using inverse bone remodeling with homogenized FE models: Comparison to micro-FE and influence of material modeling strategy

BACKGROUND AND OBJECTIVE

Measuring physiological loading conditions in vivo can be challenging, as methods are invasive or pose a high modeling effort. However, the physiological loading of bones is also imprinted in the bone microstructure due to bone (re)modeling. This information can be retrieved by inverse bone remodeling (IBR). Recently, an IBR method based on micro-finite-element (µFE) modeling was translated to homogenized-FE (hFE) to decrease computational effort and tested on the distal radius. However, this bone has a relatively simple geometry and homogeneous microstructure. Therefore, the objective of this study was to assess the agreement of hFE-based IBR with µFE-based IBR to predict hip joint loading from the head of the femur; a bone with more complex loading as well as more heterogeneous microstructure.

METHODS

hFE-based IBR was applied to a set of 19 femoral heads using four different material mapping laws. One model with a single homogeneous material for both trabecular and cortical volume and three models with a separated cortex and either homogeneous, density-dependent inhomogeneous, or density and fabric-dependent orthotropic material. Three different evaluation regions (full bone, trabecular bone only, head region only) were defined, in which IBR was applied. µFE models were created for the same bones, and the agreement of the predicted hip joint loading history obtained from hFE and µFE models was evaluated. The loading history was discretized using four unit load cases.

RESULTS

The computational time for FE solving was decreased on average from 500 h to under 1 min (CPU time) when using hFE models instead of µFE models. Using more information in the material model in the hFE models led to a better prediction of hip joint loading history. Inhomogeneous and inhomogeneous orthotropic models gave the best agreement to µFE-based IBR (RMSE% <14%). The evaluation region only played a minor role.

CONCLUSIONS

hFE-based IBR was able to reconstruct the dominant joint loading of the femoral head in agreement with µFE-based IBR and required considerably lower computational effort. Results indicate that cortical and trabecular bone should be modeled separately and at least density-dependent inhomogeneous material properties should be used with hFE models of the femoral head to predict joint loading.

A Density-Dependent Target Stimulus for Inverse Bone (Re)modeling with Homogenized Finite Element Models

Inverse bone (re)modeling (IBR) can infer physiological loading conditions from the bone microstructure. IBR scales unit loads, imposed on finite element (FE) models of a bone, such that the trabecular microstructure is homogeneously loaded and the difference to a target stimulus is minimized. Micro-FE (µFE) analyses are typically used to model the microstructure, but computationally more efficient, homogenized FE (hFE) models, where the microstructure is replaced by an equivalent continuum, could be used instead. However, also the target stimulus has to be translated from the tissue to the continuum level. In this study, a new continuum-level target stimulus relating relative bone density and strain energy density is proposed. It was applied using different types of hFE models to predict the physiological loading of 21 distal radii sections, which was subsequently compared to µFE-based IBR. The hFE models were able to correctly identify the dominant load direction and showed a high correlation of the predicted forces, but mean magnitude errors ranged from − 14.7 to 26.6% even for the best models. While µFE-based IBR can still be regarded as a gold standard, hFE-based IBR enables faster predictions, the usage of more sophisticated boundary conditions, and the usage of clinical images.

The effect of different grasping types on strain distributions in the trapezium of bonobos (Pan paniscus)

The thumb has played a key role in primate evolution due to its involvement in grasping and manipulation. A large component of this wide functionality is by virtue of the uniquely shaped trapeziometacarpal (TMC) joint. This TMC joint allows for a broad range of functional positions, but how its bone structure is adapted to withstand such a large variety of loading regimes is poorly understood. Here, we outline a novel, integrated finite element - micro finite element (FE-µFE) workflow to analyse strain distributions across the internal bony architecture. We have applied this modelling approach to study functional adaptation in the bonobo thumb. More specifically, the approach allows us to evaluate how strain is distributed through the trapezium upon loading of its distal articular facet. As loading conditions, we use pressure distributions for different types of grasping that were estimated in a previous study. Model evaluation shows that the simulated strain values fall within realistic boundaries of the mechanical response of bone. The results show that the strain distributions between the simulated grasps are highly similar, with dissipation towards the proximo-ulnar cluster of trabeculae regardless of trapezial bone architecture. This study presents an innovative FE-µFE approach to simulating strain distributions, and yields insight in the functional adaptation of the TMC joint in bonobos.

Bone Mechanoregulation Allows Subject-Specific Load Estimation Based on Time-Lapsed Micro-CT and HR-pQCT in Vivo

Patients at high risk of fracture due to metabolic diseases frequently undergo long-term antiresorptive therapy. However, in some patients, treatment is unsuccessful in preventing fractures or causes severe adverse health outcomes. Understanding load-driven bone remodelling, i.e., mechanoregulation, is critical to understand which patients are at risk for progressive bone degeneration and may enable better patient selection or adaptive therapeutic intervention strategies. Bone microarchitecture assessment using high-resolution peripheral quantitative computed tomography (HR-pQCT) combined with computed mechanical loads has successfully been used to investigate bone mechanoregulation at the trabecular level. To obtain the required mechanical loads that induce local variances in mechanical strain and cause bone remodelling, estimation of physiological loading is essential. Current models homogenise strain patterns throughout the bone to estimate load distribution in vivo, assuming that the bone structure is in biomechanical homoeostasis. Yet, this assumption may be flawed for investigating alterations in bone mechanoregulation. By further utilising available spatiotemporal information of time-lapsed bone imaging studies, we developed a mechanoregulation-based load estimation (MR) algorithm. MR calculates organ-scale loads by scaling and superimposing a set of predefined independent unit loads to optimise measured bone formation in high-, quiescence in medium-, and resorption in low-strain regions. We benchmarked our algorithm against a previously published load history (LH) algorithm using synthetic data, micro-CT images of murine vertebrae under defined experimental in vivo loadings, and HR-pQCT images from seven patients. Our algorithm consistently outperformed LH in all three datasets. In silico-generated time evolutions of distal radius geometries (n = 5) indicated significantly higher sensitivity, specificity, and accuracy for MR than LH (p < 0.01). This increased performance led to substantially better discrimination between physiological and extra-physiological loading in mice (n = 8). Moreover, a significantly (p < 0.01) higher association between remodelling events and computed local mechanical signals was found using MR [correct classification rate (CCR) = 0.42] than LH (CCR = 0.38) to estimate human distal radius loading. Future applications of MR may enable clinicians to link subtle changes in bone strength to changes in day-to-day loading, identifying weak spots in the bone microstructure for local intervention and personalised treatment approaches.

Metacarpophalangeal joint loads during bonobo locomotion: model predictions versus proxies

The analysis of internal trabecular and cortical bone has been an informative tool for drawing inferences about behaviour in extant and fossil primate taxa. Within the hand, metacarpal bone architecture has been shown to correlate well with primate locomotion; however, the extent of morphological differences across taxa is unexpectedly small given the variability in hand use. One explanation for this observation is that the activity-related differences in the joint loads acting on the bone are simply smaller than estimated based on commonly used proxies (i.e. external loading and joint posture), which neglect the influence of muscle forces. In this study, experimental data and a musculoskeletal finger model are used to test this hypothesis by comparing differences between climbing and knuckle-walking locomotion of captive bonobos (Pan paniscus) based on (i) joint load magnitude and direction predicted by the models and (ii) proxy estimations. The results showed that the activity-related differences in predicted joint loads are indeed much smaller than the proxies would suggest, with joint load magnitudes being almost identical between the two locomotor modes. Differences in joint load directions were smaller but still evident, indicating that joint load directions might be a more robust indicator of variation in hand use than joint load magnitudes. Overall, this study emphasizes the importance of including muscular forces in the interpretation of skeletal remains and promotes the use of musculoskeletal models for correct functional interpretations.

Musculoskeletal models of a human and bonobo finger: parameter identification and comparison to in vitro experiments

Introduction

Knowledge of internal finger loading during human and non-human primate activities such as tool use or knuckle-walking has become increasingly important to reconstruct the behaviour of fossil hominins based on bone morphology. Musculoskeletal models have proven useful for predicting these internal loads during human activities, but load predictions for non-human primate activities are missing due to a lack of suitable finger models. The main goal of this study was to implement both a human and a representative non-human primate finger model to facilitate comparative studies on metacarpal bone loading. To ensure that the model predictions are sufficiently accurate, the specific goals were: (1) to identify species-specific model parameters based on in vitro measured fingertip forces resulting from single tendon loading and (2) to evaluate the model accuracy of predicted fingertip forces and net metacarpal bone loading in a different loading scenario.

Materials & Methods

Three human and one bonobo (Pan paniscus) fingers were tested in vitro using a previously developed experimental setup. The cadaveric fingers were positioned in four static postures and load was applied by attaching weights to the tendons of the finger muscles. For parameter identification, fingertip forces were measured by loading each tendon individually in each posture. For the evaluation of model accuracy, the extrinsic flexor muscles were loaded simultaneously and both the fingertip force and net metacarpal bone force were measured. The finger models were implemented using custom Python scripts. Initial parameters were taken from literature for the human model and own dissection data for the bonobo model. Optimized model parameters were identified by minimizing the error between predicted and experimentally measured fingertip forces. Fingertip forces and net metacarpal bone loading in the combined loading scenario were predicted using the optimized models and the remaining error with respect to the experimental data was evaluated.

Results

The parameter identification procedure led to minor model adjustments but considerably reduced the error in the predicted fingertip forces (root mean square error reduced from 0.53/0.69 N to 0.11/0.20 N for the human/bonobo model). Both models remained physiologically plausible after the parameter identification. In the combined loading scenario, fingertip and net metacarpal forces were predicted with average directional errors below 6° and magnitude errors below 12%.

Conclusions

This study presents the first attempt to implement both a human and non-human primate finger model for comparative palaeoanthropological studies. The good agreement between predicted and experimental forces involving the action of extrinsic flexors—which are most relevant for forceful grasping—shows that the models are likely sufficiently accurate for comparisons of internal loads occurring during human and non-human primate manual activities.