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Zhou, Z., Li, X. & Kleiven, S. (2025). Surface-based versus voxel-based finite element head models: comparative analyses of strain responses. Biomechanics and Modeling in Mechanobiology
Open this publication in new window or tab >>Surface-based versus voxel-based finite element head models: comparative analyses of strain responses
2025 (English)In: Biomechanics and Modeling in Mechanobiology, ISSN 1617-7959, E-ISSN 1617-7940Article in journal (Refereed) Published
Abstract [en]

Finite element (FE) models of the human head are important injury assessment tools but developing a high-quality, hexahedral-meshed FE head model without compromising geometric accuracy is a challenging task. Important brain features, such as the cortical folds and ventricles, were captured only in a handful of FE head models that were primarily developed from two meshing techniques, i.e., surface-based meshing with conforming elements to capture the interfacial boundaries and voxel-based meshing by converting the segmented voxels into elements with and without mesh smoothing. Despite these advancements, little knowledge existed of how similar the strain responses were between surface- and voxel-based FE head models. This study uniquely addressed this gap by presenting three anatomically detailed models - a surface-based model with conforming meshes to capture the cortical folds-subarachnoid cerebrospinal fluid and brain-ventricle interfaces, and two voxel-based models (with and without mesh smoothing) - derived from the same imaging dataset. All numerical settings in the three models were exactly the same, except for the meshes. These three models were employed to simulate head impacts. The results showed that, when calculating commonly used injury metrics, including the percentile strains below the maximum (e.g., 99 percentile strain) and the volume of brain element with the strain over certain thresholds, the responses of the three models were virtually identical. Different strain patterns existed between the surface- and the voxel-based models at the interfacial boundary (e.g., sulci and gyri in the cortex, regions adjacent to the falx and tentorium) with strain differences exceeding 0.1, but remarkable similarities were noted at the non-interfacial region. The mesh smoothing procedure marginally reduced the strain discrepancies between the voxel- and surface-based model. This study yielded new quantitative insights into the general similarity in the strain responses between the surface- and voxel-based FE head models and underscored that caution should be exercised when using the strain at the interface to predict injury.

Place, publisher, year, edition, pages
Springer Nature, 2025
Keywords
Traumatic brain injury, Finite element head models, Hexahedral mesh techniques, Surface- and voxel-based meshing, Brain strain
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:kth:diva-361614 (URN)10.1007/s10237-025-01940-z (DOI)001441637800001 ()40067579 (PubMedID)2-s2.0-105000071382 (Scopus ID)
Note

QC 20250324

Available from: 2025-03-24 Created: 2025-03-24 Last updated: 2025-05-27Bibliographically approved
Lindgren, N., Huang, Q., Yuan, Q., Lin, M., Wang, P., Pipkorn, B., . . . Li, X. (2025). Toward systematic finite element reconstructions of accidents involving vulnerable road users. Traffic Injury Prevention
Open this publication in new window or tab >>Toward systematic finite element reconstructions of accidents involving vulnerable road users
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2025 (English)In: Traffic Injury Prevention, ISSN 1538-9588, E-ISSN 1538-957XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

To combat the global fatality rates among vulnerable road users (VRUs), prioritizing research on head injury mechanisms and human tolerance levels in vehicle-to-VRU traffic collisions is imperative. A foundational step for VRU injury prevention is often to create virtual reconstructions of real-world collisions. Thus, efficient and trustworthy reconstruction tools are needed to make use of recent advances in accident data collection routines and Finite Element (FE) human body modeling. In this study, a comprehensive and streamlined reconstruction methodology, starting from a video-recorded accident, has been developed. The workflow, that includes state-of-the-art tools for personalization of human body models (HBMs) and vehicles, was evaluated and demonstrated through 20 real-world VRU collision cases. The FE models successfully replicated the vehicle damage that was observed in on-scene photographs of the post-impact vehicle, as well as impact kinematics captured in dash cam or surveillance recordings. The findings highlight how video evidence can considerably narrow down the number of plausible impact scenarios and raise the credibility of virtual reconstructions of real-world VRU collision events. More importantly, this study demonstrates how, with an efficient and systematic methodology, FE might be feasible also for large-scale VRU accident datasets.

Place, publisher, year, edition, pages
Informa UK Limited, 2025
National Category
Applied Mechanics
Research subject
Technology and Health; Applied and Computational Mathematics, Numerical Analysis
Identifiers
urn:nbn:se:kth:diva-359625 (URN)10.1080/15389588.2024.2449257 (DOI)001411806100001 ()2-s2.0-85216745142 (Scopus ID)
Funder
Vinnova, 2019-03386Swedish Research Council, 2020-04724Swedish Research Council, 2020-04496
Available from: 2025-02-06 Created: 2025-02-06 Last updated: 2025-04-25Bibliographically approved
Huang, Q., Lindgren, N., Zhou, Z., Li, X. & Kleiven, S. (2024). A method for generating case-specific vehicle models from a single-view vehicle image for accurate pedestrian injury reconstructions. Accident Analysis and Prevention, 200, Article ID 107555.
Open this publication in new window or tab >>A method for generating case-specific vehicle models from a single-view vehicle image for accurate pedestrian injury reconstructions
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2024 (English)In: Accident Analysis and Prevention, ISSN 0001-4575, E-ISSN 1879-2057, Vol. 200, article id 107555Article in journal (Refereed) Published
Abstract [en]

Developing vehicle finite element (FE) models that match real accident-involved vehicles is challenging. This is related to the intricate variety of geometric features and components. The current study proposes a novel method to efficiently and accurately generate case-specific buck models for car-to-pedestrian simulations. To achieve this, we implemented the vehicle side-view images to detect the horizontal position and roundness of two wheels to rectify distortions and deviations and then extracted the mid-section profiles for comparative calculations against baseline vehicle models to obtain the transformation matrices. Based on the generic buck model which consists of six key components and corresponding matrices, the case-specific buck model was generated semi-automatically based on the transformation metrics. Utilizing this image-based method, a total of 12 vehicle models representing four vehicle categories including family car (FCR), Roadster (RDS), small Sport Utility Vehicle (SUV), and large SUV were generated for car-to-pedestrian collision FE simulations in this study. The pedestrian head trajectories, total contact forces, head injury criterion (HIC), and brain injury criterion (BrIC) were analyzed comparatively. We found that, even within the same vehicle category and initial conditions, the variation in wrap around distance (WAD) spans 84–165 mm, in HIC ranges from 98 to 336, and in BrIC fluctuates between 1.25 and 1.46. These findings highlight the significant influence of vehicle frontal shape and underscore the necessity of using case-specific vehicle models in crash simulations. The proposed method provides a new approach for further vehicle structure optimization aiming at reducing pedestrian head injury and increasing traffic safety.

Place, publisher, year, edition, pages
Elsevier BV, 2024
Keywords
Car-to-pedestrian collision, Case-specific buck, Finite element simulations, Head injury, Impact bio-mechanics
National Category
Vehicle and Aerospace Engineering
Identifiers
urn:nbn:se:kth:diva-344929 (URN)10.1016/j.aap.2024.107555 (DOI)001223503900001 ()38531282 (PubMedID)2-s2.0-85188682260 (Scopus ID)
Note

QC 20240404

Available from: 2024-04-03 Created: 2024-04-03 Last updated: 2025-03-12Bibliographically approved
Yuan, Q., Li, X., Zhou, Z. & Kleiven, S. (2024). A novel framework for video-informed reconstructions of sports accidents: A case study correlating brain injury pattern from multimodal neuroimaging with finite element analysis. Brain Multiphysics, 6, Article ID 100085.
Open this publication in new window or tab >>A novel framework for video-informed reconstructions of sports accidents: A case study correlating brain injury pattern from multimodal neuroimaging with finite element analysis
2024 (English)In: Brain Multiphysics, E-ISSN 2666-5220, Vol. 6, article id 100085Article in journal (Refereed) Published
Abstract [en]

Ski racing is a high-risk sport for traumatic brain injury. A better understanding of the injury mechanism and the development of effective protective equipment remains central to resolving this urgency. Finite element (FE) models are useful tools for studying biomechanical responses of the brain, especially in real-world ski accidents. However, real-world accidents are often captured by handheld monocular cameras; the videos are shaky and lack depth information, making it difficult to estimate reliable impact velocities and posture which are critical for injury prediction. Introducing novel computer vision and deep learning algorithms offers an opportunity to tackle this challenge. This study proposes a novel framework for estimating impact kinematics from handheld, shaky monocular videos of accidents to inform personalized impact simulations. The utility of this framework is demonstrated by reconstructing a ski accident, in which the extracted kinematics are input to a neuroimaging-informed, personalized FE model. The FE-derived responses are compared with imaging-identified brain injury sites of the victim. The results suggest that maximum principal strain may be a useful metric for brain injury. This study demonstrates the potential of video-informed accident reconstructions combined with personalized FE modeling to evaluate individual brain injury. Statement of significance: Reconstructing real-world sports accidents combined with finite element (FE) models presents a unique opportunity to study brain injuries, as it enables simulating complex loading conditions experienced in reality. However, a significant challenge lies in accurately obtaining kinematics from the often shaky, handheld video footage of such accidents. We propose a novel framework that bridges the gap between real-world accidents and video-informed injury predictions. By integrating video analysis, 3D kinematics estimation, and personalized FE simulation, we extract accurate impact kinematics of a ski accident captured from handheld shaky monocular videos to inform personalized impact simulations, predicting the injury pathology identified by multimodal neuroimaging. This study provides important guidance on how best to estimate impact conditions from video-recorded accidents, opening new opportunities to better inform the biomechanical study of head trauma with improved boundary conditions.

Place, publisher, year, edition, pages
Elsevier BV, 2024
Keywords
Computer vision, Kinematics estimation, Personalized finite element model, Sports accidents, Traumatic brain injury
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:kth:diva-341761 (URN)10.1016/j.brain.2023.100085 (DOI)2-s2.0-85179804551 (Scopus ID)
Note

QC 20240102

Available from: 2024-01-02 Created: 2024-01-02 Last updated: 2024-01-02Bibliographically approved
Zhou, Z., Fahlstedt, M., Li, X. & Kleiven, S. (2024). Computational helmet ranking outcome is affected by the choice of injury metrics. In: 2024 IRCOBI Conference Proceedings - International Research Council on the Biomechanics of Injury: . Paper presented at 2024 International Research Council on the Biomechanics of Injury, IRCOBI 2024, Stockholm, Sweden, Sep 11 2024 - Sep 13 2024 (pp. 486-487). International Research Council on the Biomechanics of Injury
Open this publication in new window or tab >>Computational helmet ranking outcome is affected by the choice of injury metrics
2024 (English)In: 2024 IRCOBI Conference Proceedings - International Research Council on the Biomechanics of Injury, International Research Council on the Biomechanics of Injury , 2024, p. 486-487Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
International Research Council on the Biomechanics of Injury, 2024
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:kth:diva-354308 (URN)2-s2.0-85204502616 (Scopus ID)
Conference
2024 International Research Council on the Biomechanics of Injury, IRCOBI 2024, Stockholm, Sweden, Sep 11 2024 - Sep 13 2024
Note

QC 20241004

Available from: 2024-10-02 Created: 2024-10-02 Last updated: 2024-10-04Bibliographically approved
Chen, S., Kleiven, S. & Li, X. (2024). Development of a 2-Month-Old Pediatric Whole Body Finite Element Model. In: 2024 IRCOBI Conference Proceedings - International Research Council on the Biomechanics of Injury: . Paper presented at 2024 International Research Council on the Biomechanics of Injury, IRCOBI 2024, Stockholm, Sweden, Sep 11 2024 - Sep 13 2024 (pp. 1209-1210). International Research Council on the Biomechanics of Injury
Open this publication in new window or tab >>Development of a 2-Month-Old Pediatric Whole Body Finite Element Model
2024 (English)In: 2024 IRCOBI Conference Proceedings - International Research Council on the Biomechanics of Injury, International Research Council on the Biomechanics of Injury , 2024, p. 1209-1210Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
International Research Council on the Biomechanics of Injury, 2024
National Category
Medical Engineering
Identifiers
urn:nbn:se:kth:diva-354314 (URN)2-s2.0-85204488161 (Scopus ID)
Conference
2024 International Research Council on the Biomechanics of Injury, IRCOBI 2024, Stockholm, Sweden, Sep 11 2024 - Sep 13 2024
Note

QC 20241003

Available from: 2024-10-02 Created: 2024-10-02 Last updated: 2024-10-03Bibliographically approved
Lindgren, N., Yuan, Q., Pipkorn, B., Kleiven, S. & Li, X. (2024). Development of personalizable female and male pedestrian SAFER human body models. Traffic Injury Prevention, 25(2), 182-193
Open this publication in new window or tab >>Development of personalizable female and male pedestrian SAFER human body models
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2024 (English)In: Traffic Injury Prevention, ISSN 1538-9588, E-ISSN 1538-957X, Vol. 25, no 2, p. 182-193Article in journal (Refereed) Published
Abstract [en]

ObjectivesVulnerable road users are globally overrepresented as victims of road traffic injuries. Developing biofidelic male and female pedestrian human body models (HBMs) that represent diverse anthropometries is essential to enhance road safety and propose intervention strategies.MethodsIn this study, 50th percentile male and female pedestrians of the SAFER HBM were developed via a newly developed image registration-based mesh morphing framework. The performance of the HBMs was evaluated by means of a set of cadaver experiments, involving subjects struck laterally by a generic sedan buck.ResultsIn simulated whole-body pedestrian collisions, the personalized HBMs effectively replicate trajectories of the head and lower body regions, as well as head kinematics, in lateral impacts. The results also demonstrate the personalization framework's capacity to generate personalized HBMs with reliable mesh quality, ensuring robust simulations.ConclusionsThe presented pedestrian HBMs and personalization framework provide robust means to reconstruct and evaluate head impacts in pedestrian-to-vehicle collisions thoroughly and accurately.

Place, publisher, year, edition, pages
Informa UK Limited, 2024
Keywords
Human body model, pedestrian protection, morphing, impact biomechanics
National Category
Vehicle and Aerospace Engineering
Identifiers
urn:nbn:se:kth:diva-342335 (URN)10.1080/15389588.2023.2281280 (DOI)001126484200001 ()38095596 (PubMedID)2-s2.0-85179706101 (Scopus ID)
Note

QC 20240116

Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2025-04-25Bibliographically approved
Yuan, Q., Lindgren, N., Li, X. & Kleiven, S. (2024). End-to-End Workflow for Finite Element Accident Reconstruction: coupling Video-Based Human Pose Estimation with HBM Personalisation and Positioning. In: 2024 IRCOBI Conference Proceedings - International Research Council on the Biomechanics of Injury: . Paper presented at 2024 International Research Council on the Biomechanics of Injury, IRCOBI 2024, Stockholm, Sweden, Sep 11 2024 - Sep 13 2024 (pp. 1147-1148). International Research Council on the Biomechanics of Injury
Open this publication in new window or tab >>End-to-End Workflow for Finite Element Accident Reconstruction: coupling Video-Based Human Pose Estimation with HBM Personalisation and Positioning
2024 (English)In: 2024 IRCOBI Conference Proceedings - International Research Council on the Biomechanics of Injury, International Research Council on the Biomechanics of Injury , 2024, p. 1147-1148Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
International Research Council on the Biomechanics of Injury, 2024
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:kth:diva-354302 (URN)2-s2.0-85204488259 (Scopus ID)
Conference
2024 International Research Council on the Biomechanics of Injury, IRCOBI 2024, Stockholm, Sweden, Sep 11 2024 - Sep 13 2024
Note

QC 20241004

Available from: 2024-10-02 Created: 2024-10-02 Last updated: 2024-10-04Bibliographically approved
Li, X., von Schantz, A., Fahlstedt, M. & Halldin, P. (2024). Evaluating child helmet protection and testing standards: A study using PIPER child head models aged 1.5, 3, 6, and 18 years. PLOS ONE, 19(1 January), Article ID e0286827.
Open this publication in new window or tab >>Evaluating child helmet protection and testing standards: A study using PIPER child head models aged 1.5, 3, 6, and 18 years
2024 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 19, no 1 January, article id e0286827Article in journal (Refereed) Published
Abstract [en]

The anatomy of children’s heads is unique and distinct from adults, with smaller and softer skulls and unfused fontanels and sutures. Despite this, most current helmet testing standards for children use the same peak linear acceleration threshold as for adults. It is unclear whether this is reasonable and otherwise what thresholds should be. To answer these questions, helmet-protected head responses for different ages are needed which is however lacking today. In this study, we apply continuously scalable PIPER child head models of 1.5, 3, and 6 years old (YO), and an upgraded 18YO to study child helmet protection under extensive linear and oblique impacts. The results of this study reveal an age-dependence trend in both global kinematics and tissue response, with younger children experiencing higher levels of acceleration and velocity, as well as increased skull stress and brain strain. These findings indicate the need for better protection for younger children, suggesting that youth helmets should have a lower linear kinematic threshold, with a preliminary value of 150g for 1.5-year-old helmets. However, the results also show a different trend in rotational kinematics, indicating that the threshold of rotational velocity for a 1.5YO is similar to that for adults. The results also support the current use of small-sized adult headforms for testing child helmets before new child headforms are available.

Place, publisher, year, edition, pages
Public Library of Science (PLoS), 2024
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:kth:diva-342384 (URN)10.1371/journal.pone.0286827 (DOI)001136266700067 ()38165876 (PubMedID)2-s2.0-85181765894 (Scopus ID)
Note

QC 20240122

Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-02-27Bibliographically approved
Pitti, E., Herling, L., Li, X., Ajne, G. & Larsson, M. (2024). Experimental Assessment of Traction Force and Associated Fetal Brain Deformation in Vacuum-Assisted Delivery. Annals of Biomedical Engineering, 53(4), 825-844
Open this publication in new window or tab >>Experimental Assessment of Traction Force and Associated Fetal Brain Deformation in Vacuum-Assisted Delivery
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2024 (English)In: Annals of Biomedical Engineering, ISSN 0090-6964, E-ISSN 1573-9686, Vol. 53, no 4, p. 825-844Article in journal (Refereed) Published
Abstract [en]

Vacuum-assisted delivery (VAD) uses a vacuum cup on the fetal scalp to apply traction during uterine contractions, assisting complicated vaginal deliveries. Despite its widespread use, VAD presents a higher risk of neonatal morbidity compared to natural vaginal delivery and biomechanical evidence for safe VAD traction forces is still limited. The aim of this study is to develop and assess the feasibility of an experimental VAD testing setup, and investigate the impact of traction forces on fetal brain deformation. A patient-specific fetal head phantom was developed and subjected to experimental VAD in two testing setups: one with manual and one with automatic force application. The skull phantom was 3D printed using multi-material Polyjet technology. The brain phantom was cast in a 3D-printed mold using a composite hydrogel, and sonomicrometry crystals were used to estimate the brain deformation in three brain regions. The experimental VADs on the fetal head phantom allowed for quantifying brain strain with traction forces up to 112 N. Consistent brain crystal movements aligned with the traction force demonstrated the feasibility of the setup. The estimated brain deformations reached up to 4% and correlated significantly with traction force ( p  < 0.05) in regions close to the suction cup. Despite limitations such as the absence of scalp modeling and a simplified strain computation, this study provides a baseline for numerical studies and supports further research to optimize the safety of VAD procedures and develop VAD training platforms.

Place, publisher, year, edition, pages
Springer Nature, 2024
National Category
Medical Modelling and Simulation
Identifiers
urn:nbn:se:kth:diva-364567 (URN)10.1007/s10439-024-03665-z (DOI)001382445700001 ()39710825 (PubMedID)2-s2.0-85212860817 (Scopus ID)
Funder
Swedish Research Council, 2021-04707KTH Royal Institute of Technology
Note

QC 20250616

Available from: 2025-06-16 Created: 2025-06-16 Last updated: 2025-06-16Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8522-4705

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