Bioengineering Laboratory, Department of Orthopaedics, Warren Alpert Medical School of Brown University and Rhode Island Hospital, CORO West, Providence, RI 02903, USA.
J Biomech. 2011 Sep 2;44(13):2516-9. doi: 10.1016/j.jbiomech.2011.06.027. Epub 2011 Jul 20.
Computational models are increasingly being used for the analysis of kinematics and contact stresses in the wrist. To this point, however, the morphology of the carpal cartilage has been modeled simply, either with non-dimensional spring elements (in rigid body spring models) or via simple bone surface extrusions (e.g. for finite element models). In this work we describe an efficient method of generating high-resolution cartilage surfaces via micro-computed tomography (μCT) and registration to CT-generated bone surface models. The error associated with μCT imaging (at 10 μm) was 0.009 mm (95% confidence interval 0.007-0.012 mm ), or ~1.6% of the cartilage thickness. Registration error averaged 0.33±0.16 mm (97.5% confidence limit of ~0.55 mm in any one direction) and 2.42±1.56° (97.5% confidence limit of ~5.5° in any direction). The technique is immediately applicable to subject-specific models driven using kinematic data obtained through in vitro testing. However, the ultimate goal would be to generate a family of cartilage surfaces that could be scaled and/or morphed for application to models from live subjects and in vivo kinematic data.
计算模型越来越多地被用于分析手腕的运动学和接触应力。然而，到目前为止，腕骨软骨的形态学只是简单地建模，要么使用无量纲的弹簧元素（在刚体弹簧模型中），要么通过简单的骨骼表面挤压（例如，用于有限元模型）。在这项工作中，我们描述了一种通过微计算机断层扫描（μCT）生成高分辨率软骨表面的有效方法，并将其与 CT 生成的骨骼表面模型进行配准。μCT 成像（10μm）的误差为 0.009 毫米（95%置信区间为 0.007-0.012 毫米），或软骨厚度的~1.6%。注册误差平均为 0.33±0.16 毫米（任何一个方向的 97.5%置信限约为 0.55 毫米）和 2.42±1.56°（任何方向的 97.5%置信限约为 5.5°）。该技术可以立即应用于使用通过体外测试获得的运动学数据驱动的特定于个体的模型。然而，最终目标是生成一系列软骨表面，可以对其进行缩放和/或变形，以应用于来自活体的模型和体内运动学数据。
