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Schaffer R, Kang YS, Marcallini A, Pipkorn B, Bolte JH, Agnew AM. Comparison of Bending Properties in Paired Human Ribs with and without Costal Cartilage. STAPP CAR CRASH JOURNAL 2024; 68:104-154. [PMID: 39704625 DOI: 10.4271/2024-22-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2024]
Abstract
Thoracic injuries, most frequently rib fractures, commonly occur in motor vehicle crashes. With an increased reliance on human body models (HBMs) for injury prediction in various crash scenarios, all thoracic tissues and structures require more comprehensive evaluation for improvement of HBMs. The objective of this study was to quantify the contribution of costal cartilage to whole rib bending properties in physical experiments. Fifteen bilateral pairs of 5th human ribs were included in this study. One rib within each pair was tested without costal cartilage while the other rib was tested with costal cartilage. All ribs were subjected to simplified A-P loading at 2 m/s until failure to simulate a frontal thoracic impact. Results indicated a statistically significant difference in force, structural stiffness, and yield strain between ribs with and without costal cartilage. On average, ribs with costal cartilage experienced a lower force but greater displacement with a longer time to fracture compared to isolated ribs. Comparisons were complicated by varying levels of calcification between costal cartilages and varying geometry with the inclusion of the costal cartilage. This study highlights the important effects of costal cartilage on rib properties and suggests an increased focus on costal cartilage in HBMs in future work.
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Affiliation(s)
- Rose Schaffer
- Injury Biomechanics Research Center, The Ohio State University
| | - Yun-Seok Kang
- Injury Biomechanics Research Center, The Ohio State University
| | | | | | - John H Bolte
- Injury Biomechanics Research Center, The Ohio State University
| | - Amanda M Agnew
- Injury Biomechanics Research Center, The Ohio State University
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2
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Robinson A, Zheng B, von Kleeck BW, Tan J, Gayzik FS. Holistic shape variation of the rib cage in an adult population. Front Bioeng Biotechnol 2024; 12:1432911. [PMID: 39359263 PMCID: PMC11445027 DOI: 10.3389/fbioe.2024.1432911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 09/06/2024] [Indexed: 10/04/2024] Open
Abstract
Traumatic injuries to the thorax are a common occurrence, and given the disparity in outcomes, injury risk is non-uniformly distributed within the population. Rib cage geometry, in conjunction with well-established biomechanical characteristics, is thought to influence injury tolerance, but quantifiable descriptions of adult rib cage shape as a whole are lacking. Here, we develop an automated pipeline to extract whole rib cage measurements from a large population and produce distributions of these measurements to assess variability in rib cage shape. Ten measurements of whole rib cage shape were collected from 1,719 individuals aged 25-45 years old including angular, linear, areal, and volumetric measures. The resulting pipeline produced measurements with a mean percent difference to manually collected measurements of 1.7% ± 1.6%, and the whole process takes 30 s per scan. Each measurement followed a normal distribution with a maximum absolute skew value of 0.43 and a maximum absolute excess kurtosis value of 0.6. Significant differences were found between the sexes (p < 0.001) in all except angular measures. Multivariate regression revealed that demographic predictors explain 29%-68% of the variance in the data. The angular measurements had the three lowest R2 values and were also the only three to have little correlation with subject stature. Unlike other measures, rib cage height had a negative correlation with BMI. Stature was the dominant demographic factor in predicting rib cage height, coronal area, sagittal area, and volume. Subject weight was the dominant demographic factor for rib cage width, depth, axial area, and angular measurements. Age was minimally important in this cohort of adults from a narrow age range. Individuals of similar height and weight had average rib cage measurements near the regression predictions, but the range of values across all subjects encompassed a large portion of their respective distributions. Our findings characterize the variability in adult rib cage geometry, including the variation within narrow demographic criteria. In future work, these can be integrated into computer aided engineering workflows to assess the influence of whole rib cage shape on the biomechanics of the adult human thorax.
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Affiliation(s)
- Andrea Robinson
- Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Virginia Tech-Wake Forest Center for Injury Biomechanics, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Bowen Zheng
- Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - B Wade von Kleeck
- Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Virginia Tech-Wake Forest Center for Injury Biomechanics, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Josh Tan
- Department of Radiology - Imaging Informatics, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - F Scott Gayzik
- Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Virginia Tech-Wake Forest Center for Injury Biomechanics, Wake Forest University School of Medicine, Winston-Salem, NC, United States
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3
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Kote VB, Frazer LL, Shukla A, Bailly A, Hicks S, Jones DA, DiSerafino DD, Davis ML, Nicolella DP. Probabilistic Finite Element Analysis of Human Rib Biomechanics: A Framework for Improved Generalizability. Ann Biomed Eng 2024:10.1007/s10439-024-03571-4. [PMID: 38955891 DOI: 10.1007/s10439-024-03571-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 06/27/2024] [Indexed: 07/04/2024]
Abstract
In dynamic impact events, thoracic injuries often involve rib fractures, which are closely related to injury severity. Previous studies have investigated the behavior of isolated ribs under impact loading conditions, but often neglected the variability in anatomical shape and tissue material properties. In this study, we used probabilistic finite element analysis and statistical shape modeling to investigate the effect of population-wide variability in rib cortical bone tissue mechanical properties and rib shape on the biomechanical response of the rib to impact loading. Using the probabilistic finite element analysis results, a response surface model was generated to rapidly investigate the biomechanical response of an isolated rib under dynamic anterior-posterior load given the variability in rib morphometry and tissue material properties. The response surface was used to generate pre-fracture force-displacement computational corridors for the overall population and a population sub-group of older mid-sized males. When compared to the experimental data, the computational mean response had a RMSE of 4.28N (peak force 94N) and 6.11N (peak force 116N) for the overall population and sub-group respectively, whereas the normalized area metric when comparing the experimental and computational corridors ranged from 3.32% to 22.65% for the population and 10.90% to 32.81% for the sub-group. Furthermore, probabilistic sensitivities were computed in which the contribution of uncertainty and variability of the parameters of interest was quantified. The study found that rib cortical bone elastic modulus, rib morphometry and cortical thickness are the random variables that produce the largest variability in the predicted force-displacement response. The proposed framework offers a novel approach for accounting biological variability in a representative population and has the potential to improve the generalizability of findings in biomechanical studies.
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Affiliation(s)
- Vivek Bhaskar Kote
- Materials Engineering, Southwest Research Institute, San Antonio, TX, USA.
| | - Lance L Frazer
- Materials Engineering, Southwest Research Institute, San Antonio, TX, USA
| | - Avani Shukla
- Mechanical and Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ashley Bailly
- Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Sydney Hicks
- College of Natural Science and Mathematics, University of Houston, Houston, TX, USA
| | | | | | | | - Daniel P Nicolella
- Materials Engineering, Southwest Research Institute, San Antonio, TX, USA
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4
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Huang Y, Holcombe SA, Wang SC, Tang J. A deep learning-based pipeline for developing multi-rib shape generative model with populational percentiles or anthropometrics as predictors. Comput Med Imaging Graph 2024; 115:102388. [PMID: 38692200 DOI: 10.1016/j.compmedimag.2024.102388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/06/2024] [Accepted: 04/18/2024] [Indexed: 05/03/2024]
Abstract
Rib cross-sectional shapes (characterized by the outer contour and cortical bone thickness) affect the rib mechanical response under impact loading, thereby influence the rib injury pattern and risk. A statistical description of the rib shapes or their correlations to anthropometrics is a prerequisite to the development of numerical human body models representing target demographics. Variational autoencoders (VAE) as anatomical shape generators remain to be explored in terms of utilizing the latent vectors to control or interpret the representativeness of the generated results. In this paper, we propose a pipeline for developing a multi-rib cross-sectional shape generative model from CT images, which consists of the achievement of rib cross-sectional shape data from CT images using an anatomical indexing system and regular grids, and a unified framework to fit shape distributions and associate shapes to anthropometrics for different rib categories. Specifically, we collected CT images including 3193 ribs, surface regular grid is generated for each rib based on anatomical coordinates, the rib cross-sectional shapes are characterized by nodal coordinates and cortical bone thickness. The tensor structure of shape data based on regular grids enable the implementation of CNNs in the conditional variational autoencoder (CVAE). The CVAE is trained against an auxiliary classifier to decouple the low-dimensional representations of the inter- and intra- variations and fit each intra-variation by a Gaussian distribution simultaneously. Random tree regressors are further leveraged to associate each continuous intra-class space with the corresponding anthropometrics of the subjects, i.e., age, height and weight. As a result, with the rib class labels and the latent vectors sampled from Gaussian distributions or predicted from anthropometrics as the inputs, the decoder can generate valid rib cross-sectional shapes of given class labels (male/female, 2nd to 11th ribs) for arbitrary populational percentiles or specific age, height and weight, which paves the road for future biomedical and biomechanical studies considering the diversity of rib shapes across the population.
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Affiliation(s)
- Yuan Huang
- Research Investigator in International Center for Automotive Medicine (ICAM), University of Michigan, USA.
| | - Sven A Holcombe
- Research Scientist in International Center for Automotive Medicine (ICAM), University of Michigan, USA
| | - Stewart C Wang
- University of Michigan of Surgery and Director of International Center for Automotive Medicine (ICAM), USA
| | - Jisi Tang
- Key Laboratory of Biorheological Science and Technology, Bioengineering College, Chongqing University, China.
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Holcombe SA, Huang Y, Derstine BA. Population trends in human rib cross-sectional shapes. J Anat 2024; 244:792-802. [PMID: 38200705 PMCID: PMC11021607 DOI: 10.1111/joa.13999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/05/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024] Open
Abstract
Rib fractures remain the most frequent thoracic injury in motor vehicle crashes. Computational human body models (HBMs) can be used to simulate these injuries and design mitigation strategies, but they require adequately detailed geometry to replicate such fractures. Due to a lack of rib cross-sectional shape data availability, most commercial HBMs use highly simplified rib sections extracted from a single individual during original HBM development. This study provides human rib shape data collected from chest CT scans of 240 females and males across the full adult age range. A cortical bone mapping algorithm extracted cross-sectional geometry from scans in terms of local periosteal position with respect to the central rib axis and local cortex thickness. Principal component analysis was used to reduce the dimensionality of these cross-sectional shape data. Linear regression found significant associations between principal component scores and subject demographics (sex, age, height, and weight) at all rib levels, and predicted scores were used to explore the expected rib cross-sectional shapes across a wide range of subject demographics. The resulting detailed rib cross-sectional shapes were quantified in terms of their total cross-sectional area and their cortical bone cross-sectional area. Average-sized female ribs were smaller in total cross-sectional area than average-sized male ribs by between 20% and 36% across the rib cage, with the greatest differences seen in the central portions of rib 6. This trend persisted although to smaller differences of 14%-29% when comparing females and males of equal intermediate weight and stature. Cortical bone cross-sectional areas were up to 18% smaller in females than males of equivalent height and weight but also reached parity in certain regions of the rib cage. Increased age from 25 to 80 years was associated with reductions in cortical bone cross-sectional area (up to 37% in females and 26% in males at mid-rib levels). Total cross-sectional area was also seen to reduce with age in females but to a lesser degree (of up to 17% in mid-rib regions). Similar regions saw marginal increases in total cross-sectional area for male ribs, indicating age affects rib cortex thickness moreso than overall rib cross-sectional size. Increased subject height was associated with increased rib total and cortical bone cross-sectional areas by approximately 25% and 15% increases, respectively, in mid-rib sections for a given 30 cm increase in height, although the magnitudes of these associations varied by sex and rib location. Increased weight was associated with approximately equal changes in both cortical bone and total cross-sectional areas in males. These effects were most prominent (around 25% increases for an addition of 50 kg) toward lower ribs in the rib cage and had only modest effects (less than 12% change) in ribs 2-4. Females saw greater increases with weight in total rib area compared to cortical bone area, of up to 21% at the eighth rib level. Results from this study show the expected shapes of rib cross-sections across the adult rib cage and across a broad range of demographics. This detailed geometry can be used to produce accurate rib models representing widely varying populations.
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Affiliation(s)
- Sven A. Holcombe
- Morphomics Analysis GroupUniversity of MichiganAnn ArborMichiganUSA
| | - Yuan Huang
- Morphomics Analysis GroupUniversity of MichiganAnn ArborMichiganUSA
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Yuan Q, Hu J, Xiao Z, Li B, Zhu X, Niu Y, Xu S. A data-mining study on the prediction of head injury in traffic accidents among vulnerable road users with varying body sizes and head anatomical characteristics. Front Bioeng Biotechnol 2024; 12:1394177. [PMID: 38745845 PMCID: PMC11091376 DOI: 10.3389/fbioe.2024.1394177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/15/2024] [Indexed: 05/16/2024] Open
Abstract
Body sizes and head anatomical characteristics play the major role in the head injuries sustained by vulnerable road users (VRU) in traffic accidents. In this study, in order to study the influence mechanism of body sizes and head anatomical characteristics on head injury, we used age, gender, height, and Body Mass Index (BMI) as characteristic parameters to develop the personalized human body multi-rigid body (MB) models and head finite element (FE) models. Next, using simulation calculations, we developed the VRU head injury dataset based on the personalized models. In the dataset, the dependent variables were the degree of head injury and the brain tissue von Mises value, while the independent variables were height, BMI, age, gender, traffic participation status, and vehicle speed. The statistical results of the dataset show that the von Mises value of VRU brain tissue during collision ranges from 4.4 kPa to 46.9 kPa at speeds between 20 and 60 km/h. The effects of anatomical characteristics on head injury include: the risk of a more serious head injury of VRU rises with age; VRU with higher BMIs has less head injury in collision accidents; height has very erratic and nonlinear impacts on the von Mises values of the VRU's brain tissue; and the severity of head injury is not significantly influenced by VRU's gender. Furthermore, we developed the classification prediction models of head injury degree and the regression prediction models of head injury response parameter by applying eight different data mining algorithms to this dataset. The classification prediction models have the best accuracy of 0.89 and the best R2 value of 0.85 for the regression prediction models.
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Affiliation(s)
- Qiuqi Yuan
- School of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
- State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha, China
- Suzhou Research Institute, Hunan University, Suzhou, China
| | - Jingzhou Hu
- Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi Xiao
- School of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
| | - Bin Li
- School of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
- State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha, China
- Suzhou Research Institute, Hunan University, Suzhou, China
| | - Xiaoming Zhu
- Shanghai Motor Vehicle Inspection Certification and Tech Innovation Center Co., Ltd., Shanghai, China
| | | | - Shiwei Xu
- School of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
- State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha, China
- Suzhou Research Institute, Hunan University, Suzhou, China
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Larsson KJ, Östh J, Iraeus J, Pipkorn B. A First Step Toward a Family of Morphed Human Body Models Enabling Prediction of Population Injury Outcomes. J Biomech Eng 2024; 146:031008. [PMID: 37943113 DOI: 10.1115/1.4064033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023]
Abstract
The injury risk in a vehicle crash can depend on occupant specific factors. Virtual crash testing using finite element human body models (HBMs) to represent occupant variability can enable the development of vehicles with improved safety for all occupants. In this study, it was investigated how many HBMs of different sizes that are needed to represent a population crash outcome through a metamodel. Rib fracture risk was used as an example occupant injury outcome. Morphed HBMs representing variability in sex, height, and weight within defined population ranges were used to calculate population variability in rib fracture risk in a frontal and a side crash. Two regression methods, regularized linear regression with second-order terms and Gaussian process regression (GPR), were used to metamodel rib fracture risk due to occupant variability. By studying metamodel predictive performance as a function of training data, it was found that constructing GPR metamodels using 25 individuals of each sex appears sufficient to model the population rib fracture risk outcome in a general crash scenario. Further, by utilizing the known outcomes in the two crashes, an optimization method selected individuals representative for population outcomes across both crash scenarios. The optimization results showed that 5-7 individuals of each sex were sufficient to create predictive GPR metamodels. The optimization method can be extended for more crashes and vehicles, which can be used to identify a family of HBMs that are generally representative of population injury outcomes in future work.
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Affiliation(s)
- Karl-Johan Larsson
- Autoliv Research, Vårgårda 447 83, Sweden; Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg 412 96, Sweden
| | - Jonas Östh
- Volvo Cars, Gothenburg 405 31, Sweden; Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg 412 96, Sweden
| | - Johan Iraeus
- Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg 412 96, Sweden
| | - Bengt Pipkorn
- Autoliv Research, Vårgårda SE-44783, Sweden; Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg 412 96, Sweden
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Larsson E, Iraeus J, Davidsson J. Investigating sources for variability in volunteer kinematics in a braking maneuver, a sensitivity analysis with an active human body model. Front Bioeng Biotechnol 2023; 11:1203959. [PMID: 37908376 PMCID: PMC10614285 DOI: 10.3389/fbioe.2023.1203959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
Occupant kinematics during evasive maneuvers, such as crash avoidance braking or steering, varies within the population. Studies have tried to correlate the response to occupant characteristics such as sex, stature, age, and BMI, but these characteristics explain no or very little of the variation. Therefore, hypothesis have been made that the difference in occupant response stems from voluntary behavior. The aim of this study was to investigate the effect from other sources of variability: in neural delay, in passive stiffness of fat, muscle tissues and skin, in muscle size and in spinal alignment, as a first step towards explaining the variability seen among occupants in evasive maneuvers. A sensitivity analysis with simulations of the SAFER Human Body Model in braking was performed, and the displacements from the simulations were compared to those of volunteers. The results suggest that the head and torso kinematics were most sensitive to spinal alignment, followed by muscle size. For head and torso vertical displacements, the range in model kinematics was comparable to the range in volunteer kinematics. However, for forward displacements, the included parameters only explain some of the variability seen in the volunteer experiment. To conclude, the results indicate that the variation in volunteer vertical kinematics could be partly attributed to the variability in human characteristics analyzed in this study, while these cannot alone explain the variability in forward kinematics. The results can be used in future tuning of HBMs, and in future volunteer studies, when further investigating the potential causes of the large variability seen in occupant kinematics in evasive maneuvers.
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Affiliation(s)
| | | | - Johan Davidsson
- Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden
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Li X, Yuan Q, Lindgren N, Huang Q, Fahlstedt M, Östh J, Pipkorn B, Jakobsson L, Kleiven S. Personalization of human body models and beyond via image registration. Front Bioeng Biotechnol 2023; 11:1169365. [PMID: 37274163 PMCID: PMC10236199 DOI: 10.3389/fbioe.2023.1169365] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 04/28/2023] [Indexed: 06/06/2023] Open
Abstract
Finite element human body models (HBMs) are becoming increasingly important numerical tools for traffic safety. Developing a validated and reliable HBM from the start requires integrated efforts and continues to be a challenging task. Mesh morphing is an efficient technique to generate personalized HBMs accounting for individual anatomy once a baseline model has been developed. This study presents a new image registration-based mesh morphing method to generate personalized HBMs. The method is demonstrated by morphing four baseline HBMs (SAFER, THUMS, and VIVA+ in both seated and standing postures) into ten subjects with varying heights, body mass indices (BMIs), and sex. The resulting personalized HBMs show comparable element quality to the baseline models. This method enables the comparison of HBMs by morphing them into the same subject, eliminating geometric differences. The method also shows superior geometry correction capabilities, which facilitates converting a seated HBM to a standing one, combined with additional positioning tools. Furthermore, this method can be extended to personalize other models, and the feasibility of morphing vehicle models has been illustrated. In conclusion, this new image registration-based mesh morphing method allows rapid and robust personalization of HBMs, facilitating personalized simulations.
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Affiliation(s)
- Xiaogai Li
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden
| | - Qiantailang Yuan
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden
| | - Natalia Lindgren
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden
| | - Qi Huang
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden
| | | | - Jonas Östh
- Volvo Cars Safety Centre, Gothenburg, Sweden
- Division of Vehicle Safety, Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Bengt Pipkorn
- Division of Vehicle Safety, Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Autoliv Research, Vargarda, Sweden
| | - Lotta Jakobsson
- Volvo Cars Safety Centre, Gothenburg, Sweden
- Division of Vehicle Safety, Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Svein Kleiven
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden
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