Head Impact Modeling to Support a Rotational Combat Helmet Drop Test

ABSTRACT Introduction The Advanced Combat Helmet (ACH) military specification (mil-spec) provides blunt impact acceleration criteria that must be met before use by the U.S. warfighter. The specification, which requires a helmeted magnesium Department of Transportation (DOT) headform to be dropped onto a steel hemispherical target, results in a translational headform impact response. Relative to translations, rotations of the head generate higher brain tissue strains. Excessive strain has been implicated as a mechanical stimulus leading to traumatic brain injury (TBI). We hypothesized that the linear constrained drop test method of the ACH specification underreports the potential for TBI. Materials and Methods To establish a baseline of translational acceleration time histories, we conducted linear constrained drop tests based on the ACH specification and then performed simulations of the same to verify agreement between experiment and simulation. We then produced a high-fidelity human head digital twin and verified that biological tissue responses matched experimental results. Next, we altered the ACH experimental configuration to use a helmeted Hybrid III headform, a freefall cradle, and an inclined anvil target. This new, modified configuration allowed both a translational and a rotational headform response. We applied this experimental rotation response to the skull of our human digital twin and compared brain deformation relative to the translational baseline. Results The modified configuration produced brain strains that were 4.3 times the brain strains from the linear constrained configuration. Conclusions We provide a scientific basis to motivate revision of the ACH mil-spec to include a rotational component, which would enhance the test’s relevance to TBI arising from severe head impacts.

Photoacoustic-MR Images Registration Based on Co-Sparse Analysis Model to Compensate for Brain Shift

Brain shift is an important obstacle for the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging systems to update the image-guided surgery systems with real-time data. However, due to the innate limitations of the current imaging modalities, accurate and real-time brain shift compensation remains as a challenging problem. In this study, application of the intra-operative photoacoustic (PA) imaging and registration of the intra-operative PA images with pre-operative brain MR images is proposed to compensate brain deformation during surgery. Finding a satisfactory multimodal image registration method is a challenging problem due to complicated and unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for PA-MR image registration which can capture the interdependency of two modalities. The proposed algorithm works based on the minimization of mapping transform by using a pair of analysis operators. These operators are learned by the alternating direction method of multipliers. The method was evaluated using experimental phantom and ex-vivo data obtained from mouse brain. The results of phantom data show about 60% and 63% improvement in root mean square error (RMSE) and target registration error (TRE) in comparison with commonly used normalized mutual information registration method. In addition, the results of mouse brain and phantom data shown more accurate performance for PA versus ultrasound imaging for brain shift calculation. Finally, by using the proposed registration method, the intra-operative PA images could become a promising tool when the brain shift invalidated pre-operative MRI.

Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents

This study evaluates the effectiveness of various widely used head injury criteria (HICs) in predicting vulnerable road user (VRU) head injuries due to road traffic accidents. Thirty-one real-world car-to-VRU impact accident cases with detailed head injury records were collected and replicated through the computational biomechanics method; head injuries observed in the analyzed accidents were reconstructed by using a finite element (FE)-multibody (MB) coupled pedestrian model [including the Total Human Model for Safety (THUMS) head–neck FE model and the remaining body segments of TNO MB pedestrian model], which was developed and validated in our previous study. Various typical HICs were used to predict head injuries in all accident cases. Pearson’s correlation coefficient analysis method was adopted to investigate the correlation between head kinematics-based injury criteria and the actual head injury of VRU; the effectiveness of brain deformation-based injury criteria in predicting typical brain injuries [such as diffuse axonal injury diffuse axonal injury (DAI) and contusion] was assessed by using head injury risk curves reported in the literature. Results showed that for head kinematics-based injury criteria, the most widely used HICs and head impact power (HIP) can accurately and effectively predict head injury, whereas for brain deformation-based injury criteria, the maximum principal strain (MPS) behaves better than cumulative strain damage measure (CSDM0.15 and CSDM0.25) in predicting the possibility of DAI. In comparison with the dilatation damage measure (DDM), MPS seems to better predict the risk of brain contusion.

A generative model of hyperelastic strain energy density functions for multiple tissue brain deformation

Purpose Estimation of brain deformation is crucial during neurosurgery. Whilst mechanical characterisation captures stress–strain relationships of tissue, biomechanical models are limited by experimental conditions. This results in variability reported in the literature. The aim of this work was to demonstrate a generative model of strain energy density functions can estimate the elastic properties of tissue using observed brain deformation. Methods For the generative model a Gaussian Process regression learns elastic potentials from 73 manuscripts. We evaluate the use of neo-Hookean, Mooney–Rivlin and 1-term Ogden meta-models to guarantee stability. Single and multiple tissue experiments validate the ability of our generative model to estimate tissue properties on a synthetic brain model and in eight temporal lobe resection cases where deformation is observed between pre- and post-operative images. Results Estimated parameters on a synthetic model are close to the known reference with a root-mean-square error (RMSE) of 0.1 mm and 0.2 mm between surface nodes for single and multiple tissue experiments. In clinical cases, we were able to recover brain deformation from pre- to post-operative images reducing RMSE of differences from 1.37 to 1.08 mm on the ventricle surface and from 5.89 to 4.84 mm on the resection cavity surface. Conclusion Our generative model can capture uncertainties related to mechanical characterisation of tissue. When fitting samples from elastography and linear studies, all meta-models performed similarly. The Ogden meta-model performed the best on hyperelastic studies. We were able to predict elastic parameters in a reference model on a synthetic phantom. However, deformation observed in clinical cases is only partly explained using our generative model.