...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Biomechanics >Statistical estimation of femur micro-architecture using optimal shape and density predictors
【24h】

Statistical estimation of femur micro-architecture using optimal shape and density predictors

机译：使用最佳形状和密度预测器对股骨微结构进行统计估计

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The personalization of trabecular micro-architecture has been recently shown to be important in patient-specific biomechanical models of the femur. However, high-resolution in vivo imaging of bone micro-architecture using existing modalities is still infeasible in practice due to the associated acquisition times, costs, and X-ray radiation exposure. In this study, we describe a statistical approach for the prediction of the femur micro-architecture based on the more easily extracted subject-specific bone shape and mineral density information. To this end, a training sample of ex vivo micro-CT images is used to learn the existing statistical relationships within the low and high resolution image data. More specifically, optimal bone shape and mineral density features are selected based on their predictive power and used within a partial least square regression model to estimate the unknown trabecular micro-architecture within the anatomical models of new subjects. The experimental results demonstrate the accuracy of the proposed approach, with average errors of 0.07 for both the degree of anisotropy and tensor norms. (C) 2015 Elsevier Ltd. All rights reserved.
机译：近来，小梁微体系结构的个性化已被证明在特定于患者的股骨生物力学模型中很重要。但是，由于相关的采集时间，成本和X射线辐射暴露，使用现有模式进行的骨骼微体系结构的高分辨率体内成像在实践中仍然不可行。在这项研究中，我们基于更容易提取的受检者特定的骨骼形状和矿物质密度信息，描述了一种预测股骨微结构的统计方法。为此，使用体外micro-CT图像的训练样本来了解低分辨率和高分辨率图像数据中的现有统计关系。更具体地，基于它们的预测能力选择最佳的骨形状和矿物质密度特征，并将其在偏最小二乘回归模型中用于估计新受试者的解剖模型内的未知小梁微结构。实验结果证明了该方法的准确性，各向异性度和张量范数的平均误差为0.07。 （C）2015 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号