Reconstruction and Quantitative Evaluation of Blunt Injury Cases by Finite Element Method.

OBJECTIVES: To reconstruct the cases of acceleration craniocerebral injury caused by blunt in forensic cases by finite element method (FEM), and to study the biomechanical mechanism and quantitative evaluation method of blunt craniocerebral injury. METHODS: Based on the established and validated finite element head model of Chinese people, the finite element model of common injury tool was established with reference to practical cases in the forensic identification, and the blunt craniocerebral injury cases were reconstructed by simulation software. The cases were evaluated quantitatively by analyzing the biomechanical parameters such as intracranial pressure, von Mises stress and the maximum principal strain of brain tissue. RESULTS: In case 1, when the left temporal parietal was hit with a round wooden stick for the first time, the maximum intracranial pressure was 359 kPa; the maximum von Mises stress of brain tissue was 3.03 kPa at the left temporal parietal; the maximum principal strain of brain tissue was 0.016 at the left temporal parietal. When the right temporal was hit with a square wooden stick for the second time, the maximum intracranial pressure was 890 kPa; the maximum von Mises stress of brain tissue was 14.79 kPa at the bottom of right temporal lobe; the maximum principal strain of brain tissue was 0.103 at the bottom of the right temporal lobe. The linear fractures occurred at the right temporal parietal skull and the right middle cranial fossa. In case 2, when the forehead and left temporal parietal were hit with a round wooden stick, the maximum intracranial pressure was 370 kPa and 1 241 kPa respectively, the maximum von Mises stress of brain tissue was 3.66 kPa and 26.73 kPa respectively at the frontal lobe and left temporal parietal lobe, and the maximum principal strain of brain tissue was 0.021 and 0.116 respectively at the frontal lobe and left temporal parietal lobe. The linear fracture occurred at the left posterior skull of the coronary suture. The damage evaluation indicators of the simulation results of the two cases exceeded their damage threshold, and the predicted craniocerebral injury sites and fractures were basically consistent with the results of the autopsy. CONCLUSIONS: The FEM can quantitatively evaluate the degree of blunt craniocerebral injury. The FEM combined with traditional method will become a powerful tool in forensic craniocerebral injury identification and will also become an effective means to realize the visualization of forensic evidence in court.

[Evaluation of brain injury caused by stick type blunt instruments based on convolutional neural network and finite element method].

The finite element method is a new method to study the mechanism of brain injury caused by blunt instruments. But it is not easy to be applied because of its technology barrier of time-consuming and strong professionalism. In this study, a rapid and quantitative evaluation method was investigated to analyze the craniocerebral injury induced by blunt sticks based on convolutional neural network and finite element method. The velocity curve of stick struck and the maximum principal strain of brain tissue (cerebrum, corpus callosum, cerebellum and brainstem) from the finite element simulation were used as the input and output parameters of the convolutional neural network The convolutional neural network was trained and optimized by using the 10-fold cross-validation method. The Mean Absolute Error (MAE), Mean Square Error (MSE), and Goodness of Fit ( R 2) of the finally selected convolutional neural network model for the prediction of the maximum principal strain of the cerebrum were 0.084, 0.014, and 0.92, respectively. The predicted results of the maximum principal strain of the corpus callosum were 0.062, 0.007, 0.90, respectively. The predicted results of the maximum principal strain of the cerebellum and brainstem were 0.075, 0.011, and 0.94, respectively. These results show that the research and development of the deep convolutional neural network can quickly and accurately assess the local brain injury caused by the sticks blow, and have important application value for understanding the quantitative evaluation and the brain injury caused by the sticks struck. At the same time, this technology improves the computational efficiency and can provide a basis reference for transforming the current acceleration-based brain injury research into a focus on local brain injury research.

Strike Velocity Prediction of Stick Blunt Instruments Based on Backpropagation Neural Network.

OBJECTIVES: To analyze and predict the striking velocity range of stick blunt instruments in different populations, and to provide basic data for the biomechanical analysis of blunt force injuries in forensic identification. METHODS: Based on the Photron FASTCAM SA3 high-speed camera, Photron FASTCAM Viewer 4.0 and SPSS 26.0 software, the tester's maximum striking velocity of stick blunt instruments and related factors were calculated and analyzed, and inputed to the backpropagation (BP) neural network for training. The trained and verified BP neural network was used as the prediction model. RESULTS: A total of 180 cases were tested and 470 pieces of data were measured. The maximum striking velocity range was 11.30-35.99 m/s. Among them, there were 122 female data, the maximum striking velocity range was 11.63-29.14 m/s; there were 348 male data, the maximum striking velocity range was 20.11-35.99 m/s. The maximum striking velocity of stick blunt instruments increased with the increase of weight and height, but there was no obvious increase trend in the male group; the maximum striking velocity decreased with age, but there was no obvious downward trend in the female group. The maximum striking velocity of stick blunt instruments has no significant correlation with the material and strike posture. The root mean square error (RMSE), the mean absolute error (MAE) and the coefficient of determination (R2) of the prediction results by using BP neural network were 2.16, 1.63 and 0.92, respectively. CONCLUSIONS: The prediction model of BP neural network can meet the demand of predicting the maximum striking velocity of different populations.

The Influence of Skull Thickness on Intracranial Biomechanical Response / 医用生物力学

Objective To study the influence of skull thickness on intracranial biomechanical parameters by finite element method. Methods The female head at 5th percentile was selected for CT scanning to construct finite element model of the head with high biofidelity,and the model was verified by reconstructed cadaver test. The finite element model of the head with different skull thickness was established, and multiple groups of tests were carried out to compare the intracranial mechanical parameters. Results The negative value of intracranial pressure was significantly affected by the decrease in skull thickness under the same head size, while the negative value of intracranial pressure was slightly affected, with an increasing trend. The shear stress and von Mises stress of brain tissues were significantly increased with skull thickness increasing. Conclusions Under the same head size, the skull thickness will affect head injury to a certain extent, and people with small skull thickness are more likely to be injured than those with large skull thickness.

[Research on thorax impact injury of children at different ages based on finite element models].

The pediatric cadaver impact experiments were reconstructed using the validated finite element(FE) models of the 3-year-old and 6-year-old children. The effect of parameters, such as hammer size, material parameters and thorax anatomical structure characteristics, on the impact mechanical responses of 3-year-old and 6-year-old pediatric thorax was discussed by designing reasonable finite element simulation experiments. The research results showed that the variation of thorax contact peak force for 3-year-old group was far larger than that of 6-year-old group when the child was impacted by hammers with different size, which meant that 3-year-old child was more sensitive to hammer size. The mechanical properties of thoracic organs had little influence on the thorax injury because of the small difference between 3-year-old and 6-year-old child in this research. During the impact, rib deformation led to different impact location and deformation of internal organs because the 3-year-old and 6-year-old children had different geometrical anatomical structures, such as different size of internal organs. Therefore, the injury of internal organs in the two groups was obviously different. It is of great significance to develop children finite element models with high biofidelity according to its real anatomical structures.

[Effect of muscle biofidelity on thoracic impact biomechanical response of a six-year-old child using finite element method].

Finite element(FE) model of thorax with high biofidelity is one of the most important methods to investigate thoracic injury mechanism because of the absence of pediatric cadaver experiments. Based on the validated thorax finite element model, the FE models with equivalent muscles and real geometric muscles were developed respectively, and the effect of muscle biofidelity on thoracic injury was analyzed with reconstructing pediatric cadaver thorax impact experiments. The simulation results showed that the thoracic impact force, the maximum displacement and the maximum von-Mises stress of FE models with equivalent muscles were slightly greater than those from FE models with real geometric muscles, and the maximum principal strains of heart and lung were a little lower. And the correlation coefficient between cadaver corridor and FE model with real muscles was also greater than that between cadaver corridor and FE model with equivalent muscles. As a conclusion, the FE models with real geometric muscles can accurately reflect the biomechanical response of thorax during the impact.