An investigation into axial impacts of the cervical spine using digital image correlation

Deep learning enables accurate soft tissue tendon deformation estimation in vivo via ultrasound imaging

Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been important in biomechanics research where measuring in vitro and in vivo tissue deformations are important for evaluating tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of images collected in vivo called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging, in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare benchmark StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and applied forces, highlighting the potential for StrainNet to be a valuable tool for assessing rehabilitation strategies or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.

Electromyography-Assisted Neuromusculoskeletal Models Can Estimate Physiological Muscle Activations and Joint Moments Across the Neck Before Impacts

Knowledge of neck muscle activation strategies before sporting impacts is crucial for investigating mechanisms of severe spinal injuries. However, measurement of muscle activations during impacts is experimentally challenging and computational estimations are not often guided by experimental measurements. We investigated neck muscle activations before impacts with the use of electromyography (EMG)-assisted neuromusculoskeletal models. Kinematics and EMG recordings from four major neck muscles of a rugby player were experimentally measured during rugby activities. A subject-specific musculoskeletal model was created with muscle parameters informed from MRI measurements. The model was used in the calibrated EMG-informed neuromusculoskeletal modeling toolbox and three neural solutions were compared: (i) static optimization (SO), (ii) EMG-assisted (EMGa), and (iii) MRI-informed EMG-assisted (EMGaMRI). EMGaMRI and EMGa significantly (p < 0.01) outperformed SO when tracking cervical spine net joint moments from inverse dynamics in flexion/extension (RMSE = 0.95, 1.14, and 2.32 N·m) but not in lateral bending (RMSE = 1.07, 2.07, and 0.84 N·m). EMG-assisted solutions generated physiological muscle activation patterns and maintained experimental cocontractions significantly (p < 0.01) outperforming SO, which was characterized by saturation and nonphysiological "on-off" patterns. This study showed for the first time that physiological neck muscle activations and cervical spine net joint moments can be estimated without assumed a priori objective criteria before impacts. Future studies could use this technique to provide detailed initial loading conditions for theoretical simulations of neck injury during impacts.

MicroFE models of porcine vertebrae with induced bone focal lesions: Validation of predicted displacements with digital volume correlation

The evaluation of the local mechanical behavior as a result of metastatic lesions is fundamental for the characterization of the mechanical competence of metastatic vertebrae. Micro finite element (microFE) models have the potential of addressing this challenge through laboratory studies but their predictions of local deformation due to the complexity of the bone structure compromized by the lesion must be validated against experiments. In this study, the displacements predicted by homogeneous, linear and isotropic microFE models of vertebrae were validated against experimental Digital Volume Correlation (DVC) measurements. Porcine spine segments, with and without mechanically induced focal lesions, were tested in compression within a micro computed tomography (microCT) scanner. The displacement within the bone were measured with an optimized global DVC approach (BoneDVC). MicroFE models of the intact and lesioned vertebrae, including or excluding the growth plates, were developed from the microCT images. The microFE and DVC boundary conditions were matched. The displacements measured by the DVC and predicted by the microFE along each Cartesian direction were compared. The results showed an excellent agreement between the measured and predicted displacements, both for intact and metastatic vertebrae, in the middle of the vertebra, in those cases where the structure was not loaded beyond yield (0.69 < R² < 1.00). Models with growth plates showed the worst correlations (0.02 < R² < 0.99), while a clear improvement was observed if the growth plates were excluded (0.56 < R² < 1.00). In conclusion, these simplified models can predict complex displacement fields in the elastic regime with high reliability, more complex non-linear models should be implemented to predict regions with high deformation, when the bone is loaded beyond yield.

Type, size, and position of metastatic lesions explain the deformation of the vertebrae under complex loading conditions

BACKGROUND

Bone metastases may lead to spine instability and increase the risk of fracture. Scoring systems are available to assess critical metastases, but they lack specificity, and provide uncertain indications over a wide range, where most cases fall. The aim of this work was to use a novel biomechanical approach to evaluate the effect of lesion type, size, and location on the deformation of the metastatic vertebra.

METHOD

Vertebrae with metastases were identified from 16 human spines from a donation programme. The size and position of the metastases, and the Spine Instability Neoplastic Score (SINS) were evaluated from clinical Quantitative Computed Tomography images. Thirty-five spine segments consisting of metastatic vertebrae and adjacent healthy controls were biomechanically tested in four different loading conditions. The strain distribution over the entire vertebral bodies was measured with Digital Image Correlation. Correlations between the features of the metastasis (type, size, position and SINS) and the deformation of the metastatic vertebrae were statistically explored.

RESULTS

The metastatic type (lytic, blastic, mixed) characterizes the vertebral behaviour (Kruskal-Wallis, p = 0.04). In fact, the lytic metastases showed more critical deformation compared to the control vertebrae (average: 2-fold increase, with peaks of 14-fold increase). By contrast, the vertebrae with mixed or blastic metastases did not show a clear trend, with deformations similar or lower than the controls. Once the position of the lytic lesion with respect to the loading direction was taken into account, the size of the lesion was significantly correlated with the perturbation to the strain distribution (r² = 0.72, p < 0.001). Conversely, the SINS poorly correlated with the mechanical evidence, and only in case of lytic lesions (r² = 0.25, p < 0.0001).

CONCLUSION

These results highlight the relevance of the size and location of the lytic lesion, which are marginally considered in the current clinical scoring systems, in driving the spinal biomechanical instability. The strong correlation with the biomechanical evidence indicates that these parameters are representative of the mechanical competence of the vertebra. The improved explanatory power compared to the SINS suggests including them in future guidelines for the clinical practice.

A novel specimen shape for measurement of linear strain fields by means of digital image correlation

Strains on the surface of engineering structures or biological tissues are non-homogeneous. These strain fields can be captured by means of Digital Image Correlation (DIC). However, DIC strain field measurements are prone to noise and filtering of these fields influences measured strain gradients. This study aims to design a novel tensile test specimen showing two linear gradients, to measure full-field linear strain measurements on the surface of test specimens, and to investigate the accuracy of DIC strain measurements globally (full-field) and locally (strain gauges' positions), with and without filtering of the DIC strain fields. Three materials were employed for this study: aluminium, polymer, and bovine bone. Normalized strain gradients were introduced that are load independent and evaluated at two local positions showing 3.6 and 6.9% strain change per mm. Such levels are typically found in human bones. At these two positions, two strain gauges were applied to check the experimental strain magnitudes. A third strain gauge was applied to measure the strain in a neutral position showing no gradient. The accuracy of the DIC field measurement was evaluated at two deformation stages (at [Formula: see text] 500 and 1750 μstrain) using the root mean square error (RMSE). The RMSE over the two linear strain fields was less than 500 μstrain for both deformation stages and all materials. Gaussian low-pass filter (LPF) reduced the DIC noise between 25% and 64% on average. As well, filtering improved the accuracy of the local normalized strain gradients measurements with relative difference less than 20% and 12% for the high- and low-gradient, respectively. In summary, a novel specimen shape and methodological approach are presented which are useful for evaluating and improving the accuracy of the DIC measurement where non-homogeneous strain fields are expected such as on bone tissue due to their hierarchical structure.

Three-dimensional surface strain analyses of simulated defect and augmented spine segments: A biomechanical cadaveric study

While several studies have investigated fracture outcomes of intact vertebrae, fracture properties in metastatically-involved and augmented vertebrae are still far from understood. Consequently, this study was aimed to use 3D digital image correlation (3D-DIC) method to investigate the failure properties of spine segments with simulated metastatic lesions, segments augmented with poly(propylene fumarate) (PPF), and compare the outcomes with intact spines. To this end, biomechanical experiments accompanied by 3D-DIC were performed on spine segments consisting of three vertebrae and two intervertebral discs (IVDs) at loading rates of 0.083 mm/s, mimicking a physiological loading condition, and 200 mm/s, mimicking an impact-type loading condition such as a fall or an accident. Full-field surface strain analysis indicated PPF augmentation reduces the superior/inferior strain when compared with the defect specimens; Presence of a defect in the middle vertebra resulted in shear band fracture pattern. Failure of the superior endplates was confirmed in several defect specimens as the superior IVDs were protruding out of defects. The augmenting PPF showed lower superior/inferior surface strain values at the fast speed as compared to the slow speed. The results of our study showed a significant increase in the fracture force from slow to fast speeds (p = 0.0246). The significance of the study was to determine the fracture properties of normal, pathological, and augmented spinal segments under physiologically-relevant loading conditions. Understanding failure properties associated with either defect (i.e., metastasis lesion) or augmented (i.e., post-treatment) spine segments could potentially provide new insights on the outcome prediction and treatment planning. Additionally, this study provides new knowledge on the effect of PPF augmentation in improving fracture properties, potentially decreasing the risk of fracture in osteoporotic and metastatic spines.

A Novel Modelling Methodology Which Predicts the Structural Behaviour of Vertebral Bodies under Axial Impact Loading: A Finite Element and DIC Study

Cervical spine injuries (CSIs) arising from collisions are uncommon in contact sports, such as rugby union, but their consequences can be devastating. Several FE modelling approaches are available in the literature, but a fully calibrated and validated FE modelling framework for cervical spines under compressive dynamic-impact loading is still lacking and material properties are not adequately calibrated for such events. This study aimed to develop and validate a methodology for specimen-specific FE modelling of vertebral bodies under impact loading. Thirty-five (n = 35) individual vertebral bodies (VBs) were dissected from porcine spine segments, potted in bone cement and μCT scanned. A speckle pattern was applied to the anterior faces of the bones to allow digital image correlation (DIC), which monitored the surface displacements. Twenty-seven (n = 27) VBs were quasi-statically compressively tested to a load up to 10 kN from the cranial side. Specimen-specific FE models were developed for fourteen (n = 14) of the samples in this group. The material properties were optimised based on the experimental load-displacement data using a specimen-specific factor (kGSstatic) to calibrate a density to Young’s modulus relationship. The average calibration factor arising from this group was calculated (K¯GSstatic) and applied to a control group of thirteen (n = 13) samples. The resulting VB stiffnesses was compared to experimental findings. The final eight (n = 8) VBs were subjected to an impact load applied via a falling mass of 7.4kg at a velocity of 3.1ms⁻¹. Surface displacements and strains were acquired from the anterior VB surface via DIC, and the impact load was monitored with two load cells. Specimen-specific FE models were created for this dynamic group and material properties were assigned again based on the density–Young’s modulus relationship previously validated for static experiments, supplemented with an additional factor (KGSdynamic). The optimised conversion factor for quasi-static loading, K¯GSstatic, had an average of 0.033. Using this factor, the validation models presented an average numerical stiffness value 3.72% greater than the experimental one. From the dynamic loading experiments, the value for KGSdynamic was found to be 0.14, 4.2 times greater than K¯GSstatic. The average numerical stiffness was 2.3% greater than in the experiments. Almost all models presented similar stiffness variations and regions of maximum displacement to those observed via DIC. The developed FE modelling methodology allowed the creation of models which predicted both static and dynamic behaviour of VBs. Deformation patterns on the VB surfaces were acquired from the FE models and compared to DIC data, achieving high agreement. This methodology is now validated to be fully applied to create whole cervical spine models to simulate axial impact scenarios replicating rugby collision events.

Musculoskeletal modelling of the human cervical spine for the investigation of injury mechanisms during axial impacts

Head collisions in sport can result in catastrophic injuries to the cervical spine. Musculoskeletal modelling can help analyse the relationship between motion, external forces and internal loads that lead to injury. However, impact specific musculoskeletal models are lacking as current viscoelastic values used to describe cervical spine joint dynamics have been obtained from unrepresentative quasi-static or static experiments. The aim of this study was to develop and validate a cervical spine musculoskeletal model for use in axial impacts. Cervical spine specimens (C2-C6) were tested under measured sub-catastrophic loads and the resulting 3D motion of the vertebrae was measured. Specimen specific musculoskeletal models were then created and used to estimate the axial and shear viscoelastic (stiffness and damping) properties of the joints through an optimisation algorithm that minimised tracking errors between measured and simulated kinematics. A five-fold cross validation and a Monte Carlo sensitivity analysis were conducted to assess the performance of the newly estimated parameters. The impact-specific parameters were integrated in a population specific musculoskeletal model and used to assess cervical spine loads measured from Rugby union impacts compared to available models. Results of the optimisation showed a larger increase of axial joint stiffness compared to axial damping and shear viscoelastic parameters for all models. The sensitivity analysis revealed that lower values of axial stiffness and shear damping reduced the models performance considerably compared to other degrees of freedom. The impact-specific parameters integrated in the population specific model estimated more appropriate joint displacements for axial head impacts compared to available models and are therefore more suited for injury mechanism analysis.

‘In a blink of an eye your life can change’: experiences of players sustaining a rugby-related acute spinal cord injury

Background

Though rare, rugby union carries a risk for serious injuries such as acute spinal cord injuries (ASCI), which may result in permanent disability. Various studies have investigated injury mechanisms, prevention programmes and immediate medical management of these injuries. However, relatively scant attention has been placed on the player’s experience of such an injury and the importance of context.

Aim

The aim of this study was to explore the injury experience and its related context, as perceived by the catastrophically injured player.

Methods

A qualitative approach was followed to explore the immediate, postevent injury experience. Semi-structured interviews were conducted with 48 (n=48) players who had sustained a rugby-related ASCI.

Results

Four themes were derived from the data. Participants described the context around the injury incident, which may be valuable to help understand the mechanism of injury and potentially minimise risk. Participants also described certain contributing factors to their injury, which included descriptions of foul play and aggression, unaccustomed playing positions, pressure to perform and unpreparedness. The physical experience included signs and symptoms of ASCI that is important to recognise by first aiders, fellow teammates, coaches and referees. Lastly, participants described the emotional experience which has implications for all ASCI first responders.

Significance

All rugby stakeholders, including players, first responders, coaches and referees, may gain valuable information from the experiences of players who have sustained these injuries. This information is also relevant for rugby safety initiatives in shaping education and awareness interventions.