基于中国人体CT数据的股骨和胫骨参数化模型的开发

杜雯菁; 罗逍; 黄晗; 许述财; 张金换

doi:10.16511/j.cnki.qhdxxb.2018.26.046

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清华大学学报（自然科学版） ›› 2019, Vol. 59 ›› Issue (3) : 211-218. DOI: 10.16511/j.cnki.qhdxxb.2018.26.046

汽车工程

基于中国人体CT数据的股骨和胫骨参数化模型的开发

杜雯菁¹, 罗逍², 黄晗¹, 许述财¹, 张金换¹

作者信息 +

Development of femur and tibia parametric models based on Chinese CT scans

DU Wenjing¹, LUO Xiao², HUANG Han¹, XU Shucai¹, ZHANG Jinhuan¹

Author information +

文章历史 +

摘要

下肢长骨骨折损伤在汽车碰撞事故中常见，使用人体有限元模型可以有效开展损伤风险和机理的研究。个体特征（如性别、年龄、身高、体重等）对下肢长骨的几何形状和密质骨厚度具有显著影响。该文以男性中等尺寸有限元模型为基准模型，基于95例国内临床计算机断层扫描（computed tomography，CT）数据，通过网格投影变换和统计学分析建立了能够反映个体特征差异的股骨和胫骨的参数化模型。结果表明：外表面几何模型的平均绝对预测误差在3 mm以内，密质骨厚度模型的平均绝对预测误差在0.6 mm以内。下肢长骨长度对几何模型影响最显著，年龄和身体质量指数（body mass index，BMI）对股骨密质骨厚度具有显著影响，年龄对胫骨密质骨厚度具有显著影响。

Abstract

Fractures of the lower extremity long bones are common in motor vehicle crashes and human finite-element models are very effective in studies of lower extremity injury risks and injury mechanisms. The subject characteristics (sex, age, stature and weight) significantly affect the bone geometry and the cortical bone thickness distribution. This study used 95 Chinese clinical CTs to develop femur and tibia parametric models using a male mid-size template model through morphing, fitting, and statistical analyses. The average absolute errors in the predicted external surface geometry models were less than 3 mm and that the average absolute errors in the predicted thickness models were less than 0.6 mm. Bone length was the most significant predictor for the bone geometry models. Age and BMI were both significant in predicting the femoral cortex thickness distribution while only age was significant for predicting the tibia's thickness distribution.

导出引用

杜雯菁, 罗逍, 黄晗, 许述财, 张金换. 基于中国人体CT数据的股骨和胫骨参数化模型的开发[J]. 清华大学学报（自然科学版）. 2019, 59(3): 211-218 https://doi.org/10.16511/j.cnki.qhdxxb.2018.26.046

DU Wenjing, LUO Xiao, HUANG Han, XU Shucai, ZHANG Jinhuan. Development of femur and tibia parametric models based on Chinese CT scans[J]. Journal of Tsinghua University(Science and Technology). 2019, 59(3): 211-218 https://doi.org/10.16511/j.cnki.qhdxxb.2018.26.046

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基金

国家自然科学基金资助项目（51305223）

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收稿日期	出版日期
2018-07-15	2019-03-15
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2019-03-19

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