Objective To explore the influence of child head injury under different impact angles by applying the finite element model of six-year-old child pedestrian as specified in the European New Car Assessment Programme (Euro NCAP). Methods Based on the finite element model of 6-year-old pedestrian with detailed anatomical structure as specified by the Euro NCAP (TB024), four groups of simulation experiments were set up to explore the mechanism of head injury in children under different impact angles. The initial position for head mass center was on the longitudinal center line of the car. The initial speed of the car was 40 km/h. The car contacted with the model from the direction of the right (0°), the front (90°), the left (180°) and the back (270°). The kinematics differences and head impact responses were compared, and injuries of the facial bone and skull were analyzed. Results Through the analysis of head contact force, acceleration of head mass center, resultant velocity of head mass center with the vehicle, head injury criterion (HIC15), facial bone fracture and skull stress distribution, it was found that the risk of head fracture and brain contusion under back impact and front impact was higher than that under side impact. The risk of head fracture and brain contusion was highest under back impact, while the lowest under side impact. Conclusions Child pedestrian head injury was the largest under back impact. The results have important application values for the assessment and development of car-pedestrian collision protection device.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Objective To investigate the injury mechanisms of three-year-old child occupants by reconstructing a real traffic accident.Methods A traffic accident case from the CIREN database was reconstructed using a vehicle finite element model and a three-year-old child occupant injury bionic model(TUST IBMs 3YO-O).The Δv,mass of the vehicle,and deformation energy were comprehensively analyzed to calculate the collision velocity of the vehicle.This accident was simulated to present injuries to a child occupant,and the injury mechanisms were analyzed in depth.Results The TUST IBMs 3YO-O fully reconstructed the injuries of the child occupant in this case.The kinematic and biomechanical responses of the children's heads differed.The biomechanical response of the internal tissues and organs in the chest cavity showed no injury,however,the result ant chest acceleration at 3 ms reached 54 g,which exceeded the threshold.Conclusions In the future,it will be necessary to adopt biomechanical parameters for occupant safety evaluations.The application of human biomechanical models with high biofidelity to reconstruct occupant injuries in traffic accidents can not only be used to observe the kinematic responses of the occupant in the accident and analyze the injury mechanisms in depth,but also to provide references for virtual testing,as well as for the research and development of child occupant protection devices and the formulation of safety regulations.

The study presents a method for the personalized biomechanical modeling of the human head and validates the generated model.Based on the TUST 50th percentile head biomechanical model,the method utilizes head CT data of the target model,and employs three-dimensional point cloud registration and free-form deformation techniques to rapidly develop a personalized head finite element model with detailed brain tissue structures.By reconstructing classic cadaver tests,it is found that the personalized head biomechanical model created by the proposed method shows a good consistency with the results of cadaver tests in kinematic and biomechanical responses.Furthermore,no significant differences are observed when compared with the head biomechanical model developed using reverse engineering method,thus verifying the effectiveness of the developed model.Consequently,the proposed method can be used to quickly construct personalized head biomechanical models with detailed anatomical structures,providing a fundamental computational analysis tool for researches in injury biomechanics,clinical medicine,and forensic identification.

Objective To compare and analyze the effect of membrane element and spring element on biomechanical responses of cervical ligaments. Methods Based on the existing 6-year-old pediatric neck finite element model, the ligaments were simulated by membrane element and spring element, respectively. Then dynamic tensile test of C4-5 vertebrae and tensile test of full cervical spine were conducted. The membrane element model was also used to simulate the bending test, and the simulation results were analyzed. Results In dynamic tensile test of C4-5 vertebral segment, the final failure force of membrane element simulation test and spring element simulation test was 1 207 N and 842 N, respectively, and their difference from the cadaver experiment was 0.6% and 30.6%, respectively. In full cervical tensile test, the difference of peak force from membrane element simulation test and cadaver experiment was 1.8%. The peak force of spring element simulation test was 484 N, and the difference from simulation test and cadaver experiment was large. The simulation result of membrane element bending test was good. Conclusions The spring element had some limitations in force simulation. The membrane element had higher biofidelity and could reflect the biomechanical response of the ligaments.

Objective To explore the effects of different skull-brain interfaces and mesh density of the cerebrospinal fluid (CSF) on dynamic responses of the brain. Methods The impact kinematics on cadaver head under rotation and translation impacts were reconstructed based on the 50th percentile adult head finite element model. The interfaces between skull and CSF, CSF and brain were modeled with different types of interfaces, which were set as sharing nodes, tied, frictionless sliding, so as to investigate the effect of different interface types on dynamic responses of the brain. Then, the interfaces between CSF, skull and brain were set as sharing nodes, while CSF was divided into single-layer and tri-layer of hexahedral element with the constant thickness of CSF, to study influences of CSF with different mesh density layers on dynamic responses of the brain. Results The intracranial pressure was highly sensitive to the interface types, while the brain response seemed to be relatively insensitive to the variation in CSF layers. Conclusions The research findings provide theoretical references for the construction of CSF and the selection of skull-brain contact interface of the head finite element model.

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.

Objective To study the constitutive model of adipose tissue at medium strain rate and its parameter inversion. Methods Based on experiments of adipose tissue mechanical properties, the compression experiment of adipose tissues was reconstructed by finite element method, and the parameters for characterizing constitutive models of adipose tissues were screened. Combined with the method of feasible direction (MFD) in optimization method, the reverse calculation for parameters of fat tissue constitutive model at medium strain rate was conducted. ResultsCompared with Ogden constitutive model, the viscoelastic constitutive model was more suitable for characterizing the mechanical response at medium strain rate (260 s-1). The parameters of the constitutive model suitable for simulation were obtained using the reverse method. Conclusions The viscoelastic constitutive model was more suitable for characterizing the mechanical response at medium strain rate. The results provide references for studying the influence of human adipose tissues on body injury in finite element simulation of vehicle collisions.

Objective To study influencing factors of renal blunt impact injury by using finite element (FE) method. Methods Based on CT images of the kidney, the kidney FE models for different age groups were constructed. The renal blunt impact test was reconstructed, and the influence of kidney material constitutive parameters, kidney tissue structure, kidney size, impact position and impact velocity on injury severity were analyzed. Results Under the same impact condition, the stress of renal cortex decreased with the kidney mass increasing, and increased with the impact velocity of the hammer increasing. The renal capsule had a certain energy absorption effect, so as to reduce the kidney stress. When the kidney was impacted, the stress of renal cortex under side impact was significantly higher than that under frontal impact. Conclusions Compared with viscoelastic constitutive model, Mooney Rivlin material constitutive model is more suitable for FE evaluation on renal injury severity. The renal injury decreases with the kidney mass increasing. The increase of impact velocity will intensify the renal injury severity. Renal capsule will reduce renal injury to a certain extent, so the existence of renal capsule structure must be considered in FE modeling of the kidney. Compared with frontal and rear impact, the renal injury severity is greater when the kidney is impacted from the lateral side.