Durability, Repeatability and Reproducibility of the NHTSA Side Impact Dummy

Donnelly, Bruce R.; Morgan, Richard M.; Eppinger, Rolf H.

doi:10.4271/831624

Cited by 25 publications

(15 citation statements)

References 4 publications

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“…Therefore, they are not repeated here. Importantly, the WHIM has achieved an overall “good” to “excellent” validation (as assessed by correlation score based on Normalized Integral Square Error (Donnelly et al 1983; Kimpara et al 2006)) at the low (~250–300 rad/s 2 for a live human volunteer), mid (~1.9–2.3 krad/s 2 for cadaveric impact tests C755-T2 and C383-T1), and high (~11.9 krad/s 2 for cadaveric test C393-T4) levels of head angular acceleration magnitudes provided important confidence of the fidelity in WHIM-estimated brain responses.…”

Section: Methodsmentioning

confidence: 99%

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Zhao

Cai

et al. 2017

Biomech Model Mechanobiol

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Reliable prediction and diagnosis of concussion is important for its effective clinical management. Previous model- based studies largely employ peak responses from a single element in a pre-selected anatomical region of interest (ROI) and utilize a single training dataset for injury prediction. A more systematic and rigorous approach is necessary to scrutinize the entire white matter (WM) ROIs as well as ROI-constrained neural tracts. To this end, we evaluated injury prediction performances of the 50 deep WM regions using predictor variables based on strains obtained from simulating the 58 reconstructed American National Football League (NFL) head impacts. To objectively evaluate performance, repeated random subsampling was employed to split the impacts into independent training and testing datasets (39 and 19 cases, respectively, with 100 trials). Univariate logistic regressions were conducted based on training datasets to compute the area under the receiver operating characteristic curve (AUC), while accuracy, sensitivity, and specificity were reported based on testing datasets. Two tract-wise injury susceptibilities were identified as the best overall via pair-wise permutation test. They had comparable AUC, accuracy, and sensitivity, with the highest values occurring in SLF (superior longitudinal fasciculus; 0.867–0.879, 84.4–85.2%, and 84.1–84.6%, respectively). Using metrics based on WM fiber strain, the most vulnerable ROIs included genu of corpus callosum, cerebral peduncle, and uncinate fasciculus, while genu and main body of corpus callosum, and SLF were among the most vulnerable tracts. Even for one un-concussed athlete, injury susceptibility of the cingulum (hippocampus) right was elevated. These findings highlight the unique injury discriminatory potentials of computational models, and may provide important insight into how best to incorporate WM structural anisotropy for investigation of brain injury.

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Section: Methodsmentioning

confidence: 99%

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Zhao

Cai

et al. 2017

Biomech Model Mechanobiol

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“…44 The technical details have previously been published to validate the THUMS (Total HUman Model for Safety). 45 Briefly, the NISE method evaluates the error measurement (EM) between a pair of time history curves in terms of phase (N-phase), amplitude (N-amp) and shape (N-shape).…”

Section: Validation Of the Dhimmentioning

confidence: 99%

Group-Wise Evaluation and Comparison of White Matter Fiber Strain and Maximum Principal Strain in Sports-Related Concussion

Zhao

Ford

et al. 2015

Journal of Neurotrauma

145

206

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Sports-related concussion is a major public health problem in the United States and yet its biomechanical mechanisms remain unclear. In vitro studies demonstrate axonal elongation as a potential injury mechanism; however, current response-based injury predictors (e.g., maximum principal strain, ε(ep)) typically do not incorporate axonal orientations. We investigated the significance of white matter (WM) fiber orientation in strain estimation and compared fiber strain (ε(n)) with ε(ep) for 11 athletes with a clinical diagnosis of concussion. Geometrically accurate subject-specific head models with high mesh quality were created based on the Dartmouth Head Injury Model (DHIM), which was successfully validated (performance categorized as "good" to "excellent"). For WM regions estimated to be exposed to high strains using a range of injury thresholds (0.09-0.28), substantial differences existed between ε(n) and ε(ep) in both distribution (Dice coefficient of 0.13-0.33) and extent (∼ 5-10-fold differences), especially at higher threshold levels and higher rotational acceleration magnitudes. For example, an average of 3.2% vs. 29.8% of WM was predicted above an optimal threshold of 0.18 established from an in vivo animal study using ε(n) and ε(ep), respectively, with an average Dice coefficient of 0.14. The distribution of WM regions with high ε(n) was consistent with typical heterogeneous patterns of WM disruptions in diffuse axonal injury, and the group-wise extent at the optimal threshold matched well with the percentage of WM voxels experiencing significant longitudinal changes of fractional anisotropy and mean diffusivity (3.2% and 3.44%, respectively) found from a separate independent study. These results suggest the significance of incorporating WM microstructural anisotropy in future brain injury studies.

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“…The model had already been validated against cadaver test data on a series of translational head impact 17 and two series of rotational impacts, 8,34 and presented high bio-fidelity. The Normalized Integral Square Error (NISE), 3 which was a method for quantitative evaluation to compare time history curves of pressures and displacements predicted by the model with those obtained from test data, evaluated brain responses predicted by THUMS brain FE model as excellent or good grades in 93% of assessed variables. A detailed description of THUMS head-brain model and its validation can be found in Kimpara et al 10 This human brain FE model was used to obtain FE-based brain injury predictors in this study.…”

Section: Fe-based Brain Injury Predictorsmentioning

confidence: 99%

Mild Traumatic Brain Injury Predictors Based on Angular Accelerations During Impacts

Kimpara¹,

Iwamoto²

2011

Ann Biomed Eng

186

132

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Although Head Injury Criterion (HIC) is an effective criterion for head injuries caused by linear acceleration such as skull fractures, no criteria for head injuries caused by rotational kinematics has been accepted as effective so far. This study proposed two criteria based on angular accelerations for Traumatic Brain Injury (TBI), which we call Rotational Injury Criterion (RIC) and Power Rotational Head Injury Criterion (PRHIC). Concussive and non-concussive head acceleration data obtained from football head impacts were utilized to develop new injury criteria. A well-validated human brain Finite Element (FE) model was employed to find out effective injury criteria for TBI. Correlation analyses were performed between the proposed criteria and FE-based brain injury predictors such as Cumulative Strain Damage Measure (CSDM), which is defined as the percent volume of the brain that exceeds a specified first principal strain threshold, proposed to predict Diffuse Axonal Injury (DAI) which is one of TBI. The RIC was significantly correlated with the CSDMs with the strain thresholds of less than 15% (R > 0.89), which might predict mild TBI. In addition, PRHIC was also strongly correlated with the CSDMs with the strain thresholds equal to or greater than 20% (R > 0.90), which might predict more severe TBI.

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Durability, Repeatability and Reproducibility of the NHTSA Side Impact Dummy

Cited by 25 publications

References 4 publications

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Group-Wise Evaluation and Comparison of White Matter Fiber Strain and Maximum Principal Strain in Sports-Related Concussion

Mild Traumatic Brain Injury Predictors Based on Angular Accelerations During Impacts

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