Search

Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes.

Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (

Most human head/brain models represent a generic adult male head/brain. They may suffer in accuracy when investigating traumatic brain injury (TBI) on a subject-specific basis. Subject-specific models can be developed from neuroimages; however, neuroimages are not typically available in practice. In this study, we establish simple and elegant regression models between brain outer surface morphology and head dimensions measured from neuroimages along with age and sex information (N = 191; 141 males and 50 females with age ranging 14-25 years). The regression models are then used to approximate subject-specific brain models by scaling a generic counterpart, without using neuroimages. Model geometrical accuracy is assessed using adjusted [Formula: see text] and absolute percentage error (e.g., 0.720 and 3.09 ± 2.38%, respectively, for brain volume when incorporating tragion-to-top). For a subset of 11 subjects (from smallest to largest in brain volume), impact-induced brain strains are compared with those from "morphed models" derived from neuroimage-based mesh warping. We find that regional peak strains from the scaled subject-specific models are comparable to those of the morphed counterparts but could be considerably different from those of the generic model (e.g., linear regression slope of 1.01-1.03 for gray and white matter regions versus 1.16-1.19, or up to ~ 20% overestimation for the smallest brain studied). These results highlight the importance of incorporating brain morphological variations in impact simulation and demonstrate the feasibility of approximating subject-specific brain models without neuroimages using age, sex, and easily measurable head dimensions. The scaled models may improve subject specificity for future TBI investigations.

Brain strain is increasingly being used in helmet design and safety performance evaluation as it is generally considered as the primary mechanism of concussion. In this study, we investigate whether different helmet designs can meaningfully alter brain strains using two commonly used metrics, peak maximum principal strain (MPS) of the whole brain and cumulative strain damage measure (CSDM). A convolutional neural network (CNN) that instantly produces detailed brain strains is first tested for accuracy for helmeted head impacts. Based on N = 144 impacts in 12 impact conditions from three random and representative helmet models, we conclude that the CNN is sufficiently accurate for helmet testing applications, for elementwise MPS (success rate of 98.6%), whole-brain peak MPS and CSDM (coefficient of determination of 0.977 and 0.980, with root mean squared error of 0.015 and 0.029, respectively). We then apply the technique to 23 football helmet models (N = 1104 impacts) to reproduce elementwise MPS. Assuming a concussion would occur when peak MPS or CSDM exceeds a threshold, we sweep their thresholds across the value ranges to evaluate the number of predicted hypothetical concussions that different helmets sustain across the impact conditions. Relative to the 12 impact conditions tested, we find that the "best" and "worst" helmets differ by an average of 22.5% in terms of predicted concussions, ranging from 0 to 42% (the latter achieved at the threshold value of 0.28 for peak MPS and 0.4 for CSDM, respectively). Such a large variation among helmets in strain-based concussion predictions demonstrate that helmet designs can still be optimized in a clinically meaningful way. The robustness and accuracy of the CNN tool also suggest its potential for routine use for helmet design and safety performance evaluation in the future. The CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains .

Tissue-level brain responses to sport-related head impacts may be stronger predictors of brain injury risk than head kinematics alone. Despite the importance of accurate impact response estimation, the influence of head morphological variations has not been properly considered due to the limited sizes and shapes of existing computational head models. In this study, we developed 101 subject-specific finite element (FE) head-brain models based on CT scans and a parametric modeling approach to estimate tissue-level brain impact responses (maximal principal strain, MPS) under three head impact conditions. Principal component analysis (PCA) was used to quantify the geometric variations, with statistically significant PCs then selected to predict MPS using a stepwise linear regression model. High adjusted R

Background and Objective It is common to combine biomechanical modeling and medical images for multimodal analyses. However, mesh-image mismatch may occur that prevents direct information exchange. To eliminate mesh-image mismatch, we develop a simple but elegant displacement voxelization technique based on image voxel corner nodes to achieve voxel-wise strain. We then apply the technique to derive dense white matter fiber strains along whole-brain tractography (∼35 k fiber tracts consisting of ∼3.3 million sampling points) resulting from head impact. Methods Displacements at image voxel corner nodes are first obtained from model simulation via scattered interpolation. Each voxel is then scaled linearly to form a unit hexahedral element. This allows convenient and efficient voxel-wise strain tensor calculation and displacement interpolation at arbitrary fiber sampling points via shape functions. Fiber strains from displacement interpolation are then compared with those from the commonly used strain tensor projection using either voxel- or element-wise strain tensors. Results Based on a synthetic displacement field, fiber strains interpolated from voxelized displacement are considerably more accurate than those from strain tensor projection relative to the prescribed ground-truth (determinant of coefficient (R2) of 1.00 and root mean squared error (RMSE) of 0.01 vs. 0.87 and 0.10, respectively). For a set of real-world reconstructed head impacts (N=53), the strain tensor projection method performs similarly poorly (R2 of 0.80–0.90 and RMSE of 0.03–0.07), with overestimation strongly correlated with strain magnitude (Pearson correlation coefficient >0.9). Up to ∼15% of the fiber strains are overestimated by more than the lower bound of a conservative injury threshold of 0.09. The percentage increases to ∼37% when halving the threshold. Voxel interpolation is also significantly more efficient (15 sec vs. 40 sec for element strain tensor projection, without parallelization). Conclusions Voxelized displacement interpolation is considerably more accurate and efficient in deriving dense white matter fiber strains than strain tensor projection. The latter generally overestimates with overestimation magnitude strongly correlating with fiber strain magnitude. Displacement voxelization is an effective technique to eliminate mesh-image mismatch and generates a convenient image representation of tissue deformation. This technique can be generalized to broadly facilitate a diverse range of image-related biomechanical problems for multimodal analyses. The convenient image format may also promote and facilitate biomechanical data sharing in the future.

PURPOSE: Accurate and efficient spine registration is crucial to success of spine image-guidance. However, changes in spine pose cause intervertebral motion that can lead to significant registration errors. In this study, we develop a geometrical rectification technique via non-linear principal component analysis (NLPCA) to achieve level-wise vertebral registration that is robust to large changes in spine pose. METHODS: We used explanted porcine spines and live pigs to develop and test our technique. Each sample was scanned with pre-operative CT (pCT) in an initial pose, and re-scanned with intraoperative stereovision (iSV) in a different surgical posture. Patient registration rectified arbitrary spinal postures in pCT and iSV into a common, neutral pose through a parameterized moving-frame approach. Topologically encoded depth projection 2D images were then generated to establish invertible point-to-pixel correspondences. Level-wise point correspondences between pCT and iSV vertebral surfaces were generated via 2D image registration. Finally, closed-form vertebral level-wise rigid registration was obtained by directly mapping 3D surface point pairs. Implanted mini screws were used as fiducial markers to measure registration accuracy. RESULTS: In 7 explanted porcine spines and 2 live animal surgeries (maximum in-spine pose change of 87.5 mm and 32.7 degrees averaged from all spines), average target registration errors (TRE) of 1.70±0.15 mm and 1.85±0.16 mm were achieved, respectively. The automated spine rectification took 3–5 mins, followed by an additional 30 secs for depth image projection and level-wise registration. CONCLUSION: Accuracy and efficiency of the proposed level-wise spine registration supports its application in human open spine surgeries. The registration framework, itself, may also be applicable to other intraoperative imaging modalities such as ultrasound and MRI, which may expand utility of the approach in spine registration in general.

Conventional kinematics-based brain injury metrics often approximate peak maximum principal strain (MPS) of the whole brain but ignore the anatomical location of occurrence. In this study, we develop effective impact kinematics consisting of peak rotational velocity and the associated rotational axis to preserve not only peak MPS but also spatially detailed MPS. A pre-computed brain response atlas (pcBRA) serves as a common reference. A training dataset (N = 3069) is used to develop a convolutional neural network (CNN) to automate impact simplification. When preserving peak MPS alone, the CNN-estimated effective peak rotational velocity achieves a coefficient of determination ( $${R}^{2}$$ ) of ~ 0.96 relative to the directly identified counterpart, far outperforming nominal peak velocity from the resultant profiles ( $${R}^{2}$$ of ~ 0.34). Impacts from a subset of data (N = 1900) are also successfully matched with pcBRA idealized impacts based on elementwise MPS, where their regression slope and Pearson correlation coefficient do not deviate from 1.0 (when identical) by more than 0.1. The CNN-estimated effective peak rotation velocity and rotational axis are sufficiently accurate for ~ 73.5% of the impacts. This is not possible for the nominal peak velocity or any other conventional injury metric. The performance may be further improved by expanding the pcBRA to include deceleration and focusing on region-wise strains. This study establishes a new avenue to reduce an arbitrary head impact into an idealized but actual “impact mode” characterized by triplets of basic kinematic variables. They retain specific physical interpretations of head impact and may be an advancement over state-of-the-art kinematics-based scalar metrics for more effective impact comparison in the future.

Intraoperative stereovision (iSV) systems are used for image data acquisition of the surgical field to facilitate image updating in image-guided surgery. We have developed an optically-tracked hand-held stereovision (HHS) system, but the calibration process was lengthy and the accuracy can be greatly affected by localization errors. This study shows the efficacy of using a rigid calibration checkerboard contained within a custom-built tracking frame for semi-automated iSV calibration. First, stereo camera parameters were calculated with a mean reprojection error of 0.28 pixels using 46 image pairs. Then, the checkerboard was calibrated to optically track all corner points. Specifically, locations of 4 outermost corner points on the checkerboard pattern were collected 93 times using a tracked stylus, and internal corner points were interpolated. Computed spacing between corner points was 15.01±0.04 mm and 15.05±0.08 mm in x and y directions, respectively, compared to the advertised spacing of 15 mm for both x and y directions. Next, we captured 169 image pairs of the tracked calibration checkerboard to reconstruct the 3D coordinates of corner points in the camera space. After, we calculated a rigid transformation between the reconstructed points and their tracked locations with a mean error of 0.31±0.02 mm. Finally, another 50 image pairs of the tracked checkerboard were used to verify the calibration accuracy and showed a mean error of 0.88±0.24 mm. These results show promise to automate the calibration process for future units and settings.

Preoperative CT (pCT) images acquired in a supine position can be inaccurate for intraoperative image guidance in open spine surgery due to alignment change between supine pCT and intraoperative prone positioning. We have developed a level-wise registration framework to compensate for the alignment change using intraoperative stereovision (iSV) data of the exposed spine. A hand-held stereovision system was developed, but the field of view was limited to 1-2 segments per image. Although multiple iSV surfaces can be combined to capture the full field based on tracking information, acquisition is limited to one snapshot at a time with minimized hand motion due to asynchronization between image and tracking data. In this study, we developed methods to concatenate iSV surfaces without relying on tracking information, and illustrate the methods using data acquired from a pig spine. To register two iSV surfaces, the 2D texture maps were registered using an optical flow algorithm, and the 3D point cloud of the second iSV surface was registered with the first using 3D spatial information of each pixel. The two registered iSV surfaces were then merged to form one composite iSV surface. Multiple iSV surfaces were stitched sequentially. Results from 4 image pairs show that 2D and 3D registration accuracy was 2.7±0.6 pixels and 1.0±0.1 mm, respectively, across 8 landmarks. The overall accuracy of the final composite surface was 0.9±0.4 mm. These preliminary data show that iSV can potentially be acquired at video rate to improve efficiency to recover the full surgical field.

Accurate vertebral segmentation is important for image-related assessment of spine pathologies. Recent spine segmentation techniques focus on deep learning-based methods. However, they typically require a large training dataset with accurate annotations, which may not be feasible in practice. To mitigate the challenge, here we propose a data augmentation scheme based on a single spine to incorporate biomechanically constrained intervertebral motion (rotation and translation) to generate a large training dataset (N=3000). Vertebrae are then segmented using a point cloud-based deep learning architecture, PointNet++, which appears not to have been applied to spine segmentation before. Spine testing samples (N=90) are first generated by applying our data augmentation technique to three separate spines. Segmentation performances are compared with two baseline data augmentation methods that do not include intervertebral motion. Then, we further evaluate our technique based on 8 spine samples from actual image acquisitions. In both cases, we show that incorporation of vertebral level-wise data augmentation improves segmentation accuracy in terms of mean Dice coefficient (e.g., 0.932 vs. 0.902 for augmented testing samples and 0.940 vs. 0.924 for acquired testing samples). These results suggest that our vertebral level-wise data augmentation is useful to facilitate deep learning-based spine segmentation, which is important especially when it is not feasible to generate accurate annotations for a large number of training samples.

Real-time dynamic simulation remains a significant challenge for spatiotemporal data of high dimension and resolution. In this study, we establish a transformer neural network (TNN) originally developed for natural language processing and a separate convolutional neural network (CNN) to estimate five-dimensional (5D) spatiotemporal brain–skull relative displacement resulting from impact (isotropic spatial resolution of 4 mm with temporal resolution of 1 ms). Sequential training is applied to train (N = 5184 samples) the two neural networks for estimating the complete 5D displacement across a temporal duration of 60 ms. We find that TNN slightly but consistently outperforms CNN in accuracy for both displacement and the resulting voxel-wise four-dimensional (4D) maximum principal strain (e.g., root mean squared error (RMSE) of ~1.0% vs. ~1.6%, with coefficient of determination, R(2) >0.99 vs. >0.98, respectively, and normalized RMSE (NRMSE) at peak displacement of 2%–3%, based on an independent testing dataset; N = 314). Their accuracies are similar for a range of real-world impacts drawn from various published sources (dummy, helmet, football, soccer, and car crash; average RMSE/NRMSE of ~0.3 mm/~4%–5% and average R(2) of ~0.98 at peak displacement). Sequential training is effective for allowing instantaneous estimation of 5D displacement with high accuracy, although TNN poses a heavier computational burden in training. This work enables efficient characterization of the intrinsically dynamic brain strain in impact critical for downstream multiscale axonal injury model simulation. This is also the first application of TNN in biomechanics, which offers important insight into how real-time dynamic simulations can be achieved across diverse engineering fields.