Methodology to measure seat belt fit in relation to skeletal geometry using an upright open MRI

Booth, Gabrielle R.; Roberts, Carolyn W.; Romani, Sarah; Mergler, Olivia; Zhang, Honglin; Pipkorn, Bengt; Siegmund, Gunter P.; Cripton, Peter A.; Forman, Jason

doi:10.1080/15389588.2022.2124805

Cited by 2 publications

(2 citation statements)

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“…Our method also allows for easy correction of models at the component level, as demonstrated by the successful correction of an 18YO PIPER head model. Our markerless method can be easily applied to personalize other existing HBMs, and it has a particular strength in generating subject-specific models when imaging data is available, such as full or partial body MRI images of the spine (Booth et al, 2022). The same pipeline used for the PIPER head (Supplementary Figure S5) can be used by segmenting CT/MRI images into binary images first.…”

Section: Geometrical Correction Capacity Facilitates Extreme Position...mentioning

confidence: 99%

“…Finite element (FE) human body models (HBMs) are becoming increasingly important numerical tools in vehicle safety for understanding injury mechanisms and developing prevention strategies (Östh et al, 2015;Boyle et al, 2019;Hu et al, 2019;Jakobsson et al, 2019;Pipkorn et al, 2019;Boyle et al, 2020;Hwang et al, 2020;Tang et al, 2020;Grebonval et al, 2021;Leledakis et al, 2021;Larsson et al, 2022;Bohman et al, 2022;Booth et al, 2022;Corrales et al, 2022;Erlinger et al, 2022;Mishra et al, 2022;Piqueras et al, 2022;Putra et al, 2022;Östh et al, 2022). HBMs have advantages over crash test dummies, such as representing varying loading directions, different anthropometries and sex, and muscle tonus (Jakobsson et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Personalization of human body models and beyond via image registration

Yuan

Lindgren

et al. 2023

Front. Bioeng. Biotechnol.

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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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Section: Geometrical Correction Capacity Facilitates Extreme Position...mentioning

confidence: 99%