Cargando…

A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms

Due to the significant effects of the human anatomical characteristics on the injury mechanism of passenger in traffic accidents, it is necessary to develop human body FEM (Finite Element Model) with detailed anatomical characteristics. However, traditional development of a human body FEM is an extr...

Descripción completa

Detalles Bibliográficos
Autores principales: Yuan, Qiuqi, Jiang, Binhui, Zhu, Xiaoming, Hu, Jingzhou, Wang, Yulong, Chou, Clifford C., Xu, Shiwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232415/
https://www.ncbi.nlm.nih.gov/pubmed/37258627
http://dx.doi.org/10.1038/s41598-023-35374-3
_version_ 1785051972292837376
author Yuan, Qiuqi
Jiang, Binhui
Zhu, Xiaoming
Hu, Jingzhou
Wang, Yulong
Chou, Clifford C.
Xu, Shiwei
author_facet Yuan, Qiuqi
Jiang, Binhui
Zhu, Xiaoming
Hu, Jingzhou
Wang, Yulong
Chou, Clifford C.
Xu, Shiwei
author_sort Yuan, Qiuqi
collection PubMed
description Due to the significant effects of the human anatomical characteristics on the injury mechanism of passenger in traffic accidents, it is necessary to develop human body FEM (Finite Element Model) with detailed anatomical characteristics. However, traditional development of a human body FEM is an extremely complicated process. In particular, the meshing of human body is a huge and time-consuming project. In this paper, a new fast methodology based on CPD (Coherent Point Drift) and RBF (Radial Basis Function) was proposed to achieve the rapid developing the FEM of human bone with detailed anatomical characteristics. In this methodology, the mesh morphing technology based the RBF was used to generate FEM mesh in the geometry extracted from the target CT (Computed Tomography) data. In order to further improve the accuracy and speed of mesh morphing, the target geometric feature points required in the mesh morphing process were realized via the rapid and automatic generation based on the point-cloud registration technology of the CPD algorithm. Finally, this new methodology was used to generate a 3-year-old ribcage FEM consisting of a total of 27,728 elements with mesh size 3–5 mm based on the THUMS (Total Human Model for Safety) adult model. In the entire process of generating this new ribcage model, it only took about 2.7 s. The average error between the new FEM and target geometries was only about 2.7 mm. This indicated that the new FEM well described the detailed anatomical characteristics of target geometry, thus importantly revealing that the mesh quality of the new FEM was basically similar to that of source FEM.
format Online
Article
Text
id pubmed-10232415
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-102324152023-06-02 A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms Yuan, Qiuqi Jiang, Binhui Zhu, Xiaoming Hu, Jingzhou Wang, Yulong Chou, Clifford C. Xu, Shiwei Sci Rep Article Due to the significant effects of the human anatomical characteristics on the injury mechanism of passenger in traffic accidents, it is necessary to develop human body FEM (Finite Element Model) with detailed anatomical characteristics. However, traditional development of a human body FEM is an extremely complicated process. In particular, the meshing of human body is a huge and time-consuming project. In this paper, a new fast methodology based on CPD (Coherent Point Drift) and RBF (Radial Basis Function) was proposed to achieve the rapid developing the FEM of human bone with detailed anatomical characteristics. In this methodology, the mesh morphing technology based the RBF was used to generate FEM mesh in the geometry extracted from the target CT (Computed Tomography) data. In order to further improve the accuracy and speed of mesh morphing, the target geometric feature points required in the mesh morphing process were realized via the rapid and automatic generation based on the point-cloud registration technology of the CPD algorithm. Finally, this new methodology was used to generate a 3-year-old ribcage FEM consisting of a total of 27,728 elements with mesh size 3–5 mm based on the THUMS (Total Human Model for Safety) adult model. In the entire process of generating this new ribcage model, it only took about 2.7 s. The average error between the new FEM and target geometries was only about 2.7 mm. This indicated that the new FEM well described the detailed anatomical characteristics of target geometry, thus importantly revealing that the mesh quality of the new FEM was basically similar to that of source FEM. Nature Publishing Group UK 2023-05-31 /pmc/articles/PMC10232415/ /pubmed/37258627 http://dx.doi.org/10.1038/s41598-023-35374-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Yuan, Qiuqi
Jiang, Binhui
Zhu, Xiaoming
Hu, Jingzhou
Wang, Yulong
Chou, Clifford C.
Xu, Shiwei
A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms
title A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms
title_full A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms
title_fullStr A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms
title_full_unstemmed A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms
title_short A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms
title_sort fast methodology for generating skeletal fem with detailed human geometric features based on cpd and rbf algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232415/
https://www.ncbi.nlm.nih.gov/pubmed/37258627
http://dx.doi.org/10.1038/s41598-023-35374-3
work_keys_str_mv AT yuanqiuqi afastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT jiangbinhui afastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT zhuxiaoming afastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT hujingzhou afastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT wangyulong afastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT choucliffordc afastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT xushiwei afastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT yuanqiuqi fastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT jiangbinhui fastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT zhuxiaoming fastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT hujingzhou fastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT wangyulong fastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT choucliffordc fastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms
AT xushiwei fastmethodologyforgeneratingskeletalfemwithdetailedhumangeometricfeaturesbasedoncpdandrbfalgorithms