论文标题
引入各向异性Minkowski功能，用于局部结构分析和近端股骨标本生物力学强度的预测
Introducing Anisotropic Minkowski Functionals for Local Structure Analysis and Prediction of Biomechanical Strength of Proximal Femur Specimens
论文作者
论文摘要
在50岁以上的成年人中，由骨质疏松症或受伤引起的骨骼脆弱性和骨折很普遍，可以降低其生活质量。因此，通过基于非侵袭性成像的方法预测股骨近端的生物力学骨强度是诊断骨质疏松症的重要目标以及估计裂缝风险。双重X射线吸收法（DXA）已用作通过骨矿物质密度（BMD）测量值评估和诊断骨强度和骨质疏松症的标准临床程序。但是，先前的研究表明，定量计算机断层扫描（QCT）可能更敏感，并且对小梁骨表征更为特异，因为它减少了周围软组织和皮质壳的重叠效应和干扰。 这项研究提出了一种新方法，以预测定量多探测器计算机断层扫描（MDCT）图像的近端股骨标本的骨强度。纹理分析方法，例如常规统计矩（BMD平均值），各向同性Minkowski功能（IMF）和各向异性Minkowski功能（AMF）用于量化小梁骨微结构的BMD特性。然后使用这些提取特征的组合使用复杂的机器学习技术，例如多污染（Multireg）和线性内核（SVRLIN）来预测股骨标本的生物力学强度。将这些特征集实现的预测性能与使用均方根误差（RMSE）使用样品的平均BMD的标准方法进行了比较。
Bone fragility and fracture caused by osteoporosis or injury are prevalent in adults over the age of 50 and can reduce their quality of life. Hence, predicting the biomechanical bone strength, specifically of the proximal femur, through non-invasive imaging-based methods is an important goal for the diagnosis of Osteoporosis as well as estimating fracture risk. Dual X-ray absorptiometry (DXA) has been used as a standard clinical procedure for assessment and diagnosis of bone strength and osteoporosis through bone mineral density (BMD) measurements. However, previous studies have shown that quantitative computer tomography (QCT) can be more sensitive and specific to trabecular bone characterization because it reduces the overlap effects and interferences from the surrounding soft tissue and cortical shell. This study proposes a new method to predict the bone strength of proximal femur specimens from quantitative multi-detector computer tomography (MDCT) images. Texture analysis methods such as conventional statistical moments (BMD mean), Isotropic Minkowski Functionals (IMF) and Anisotropic Minkowski Functionals (AMF) are used to quantify BMD properties of the trabecular bone micro-architecture. Combinations of these extracted features are then used to predict the biomechanical strength of the femur specimens using sophisticated machine learning techniques such as multiregression (MultiReg) and support vector regression with linear kernel (SVRlin). The prediction performance achieved with these feature sets is compared to the standard approach that uses the mean BMD of the specimens and multiregression models using root mean square error (RMSE).
