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Evaluation of brain injury criteria based on reliability analysis

Hazay Máté, Bojtar Impre

Acta of Bioengineering and Biomechanics

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2021

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Vol. 23, no. 1

173--185

EN

Purpose: Among the proposed brain injury metrics, Brain Injury Criteria (BrIC) is a promising tool for performing safety assessment of vehicles in the future. In this paper, the available risk curves of BrIC were re-evaluated with the use of reliability analysis and new risk curves were constructed for different injury types based on literature data of tissue-level tolerances. Moreover, the comparison of different injury metrics and their corresponding risk curves were performed. Methods: Tissue-level uncertainties of the effect and resistance were considered by random variables. The variability of the tissue-level predictors was quantified by the finite element reconstruction of 100 frontal crash tests which were performed in Simulated Injury Monitor environment. The applied tests were scaled to given BrIC magnitudes and the injury probabilities were calculated by Monte Carlo simulations. New risk curves were fitted to the observed results using Weibull and Lognormal distribution functions. Results: The available risk curves of diffuse axonal injury (DAI) could be slightly improved, and combined AIS 4+ risk curves were obtained by considering subdural hematoma and contusion as well. The performance of several injury metrics and their risk curves were evaluated based on the observed correlations with the tissue-level predictors. Conclusions: The cumulative strain damage measure and the BrIC provide the highest correlation (R2 = 0.61) and the most reliable risk curve for the evaluation of DAI. Although the observed correlation is smaller for other injury types, the BrIC and the associated reliability analysis-based risk curves seem to provide the best available method for estimating the brain injury risk for frontal crash tests.

2

Engineering optimization of decompressive craniectomy based on finite element simulations

Hazay Máté, Nagy Emese, Tóth Peter, Büki András, Bojtár Imre

Acta of Bioengineering and Biomechanics

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2020

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Vol. 22, no. 4

109--122

EN

The optimal execution of decompressive craniectomy in terms of the size and location of the skull opening is not straightforward. Our main goals are twofold: (1) constructing a design optimization method which can be applied to determine optimal skull opening for individual patient-specific cases and (2) performing a large-scale parametric optimization study to give some guidance in general about the optimal skull opening in case of oedematous brain tissue. Methods: A large number of virtual experiments performed by finite element simulations were applied to determine tendencies of tissue behaviour during surgery. The multiobjective optimization is performed by Goal Programming and Physical Programming methods. Results: Our results show that the postoperative pressure has an approximately linear dependence on the preoperative pressure and the skull opening area, while the damaged brain volume could have a more complex nonlinear dependence on the input data. Based on the averaged results of the parametric optimization study, the optimal skull opening has been determined in the function of the preoperative pressure and the relative importance of the pressure reduction. These results show that the optimal size of the unilateral skull opening is usually between 130–180 cm2 and these openings are more beneficial than the currently analysed bifrontal openings. Conclusions: The optimal skull opening is patient-specific and depends on several input data. The presented methodology can be applied to optimize surgery based on these input parameters for different injury types. Based on the results of large-scale parametric study generally applicable approximate results have been provided.

3

The probability of traumatic brain injuries based on tissue-level reliability analysis

Hazay Máté, Bojtár Imre

Acta of Bioengineering and Biomechanics

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2019

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Vol. 21, no. 1

141--152

EN

Motor vehicle crashes are one of the leading causes of traumatic brain injuries. Restraint systems of cars are evaluated by crash tests based on human tolerance data, however, the reliability of data currently used has been questioned several times in the literature due to the neglect of certain types of effects, injury types and uncertainties. Our main goal was to re-evaluate the currently applied risk curve by taking the previously neglected effects into account. Methods: In this paper, the probability of traumatic brain injury was determined by reliability analysis where different types of uncertainties are taken into account. The tissue-level response of the human brain in the case of frontal crashes was calculated by finite element analyses and the injury probability is determined by Monte Carlo simulations. Sensitivity analysis was also performed to identify which effects have considerable contribution to the injury risk. Results: Our results indicate a significantly larger injury risk than it is predicted by current safety standards. Accordingly, a new risk curve was constructed which follows a lognormal distribution with the following parameters: μLN = 6.5445 and LN = 1.1993. Sensitivity analysis confirmed that this difference primarily can be attributed to the rotational effects and tissue-level uncertainties. Conclusions: Results of the tissue-level reliability analysis enhance the belief that rotational effects are the primary cause of brain injuries. Accordingly, the use of a solely translational acceleration based injury metric contains several uncertainties which can lead to relatively high injury probabilities even if relatively small translational effects occur.