Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, Vienna, Austria.
Bone. 2013 May;54(1):133-40. doi: 10.1016/j.bone.2012.12.047. Epub 2013 Jan 10.
High resolution peripheral quantitative computed tomography (HR-pQCT) permits the non-invasive assessment of cortical and trabecular bone density, geometry, and microarchitecture. Although researchers have developed various post-processing algorithms to quantify HR-pQCT image properties, few of these techniques capture image features beyond global structure-based metrics. While 3D-texture analysis is a key approach in computer vision, it has been utilized only infrequently in HR-pQCT research. Motivated by high isotropic spatial resolution and the information density provided by HR-pQCT scans, we have developed and evaluated a post-processing algorithm that quantifies microarchitecture characteristics via texture features in HR-pQCT scans. During a training phase in which clustering was applied to texture features extracted from each voxel of trabecular bone, three distinct clusters, or trabecular microarchitecture classes (TMACs) were identified. These TMACs represent trabecular bone regions with common texture characteristics. The TMACs were then used to automatically segment the voxels of new data into three regions corresponding to the trained cluster features. Regional trabecular bone texture was described by the histogram of relative trabecular bone volume covered by each cluster. We evaluated the intra-scanner and inter-scanner reproducibility by assessing the precision errors (PE), intra class correlation coefficients (ICC) and Dice coefficients (DC) of the method on 14 ultradistal radius samples scanned on two HR-pQCT systems. DC showed good reproducibility in intra-scanner set-up with a mean of 0.870±0.027 (no unit). Even in the inter-scanner set-up the ICC showed high reproducibility, ranging from 0.814 to 0.964. In a preliminary clinical test application, the TMAC histograms appear to be a good indicator, when differentiating between postmenopausal women with (n=18) and without (n=18) prevalent fragility fractures. In conclusion, we could demonstrate that 3D-texture analysis and feature clustering seems to be a promising new HR-pQCT post-processing tool with good reproducibility, even between two different scanners.
高分辨率外周定量计算机断层扫描（HR-pQCT）允许对皮质骨和小梁骨的密度、几何形状和微结构进行非侵入性评估。尽管研究人员已经开发了各种后处理算法来量化 HR-pQCT 图像特性，但这些技术很少能够捕捉到超出基于全局结构的度量标准的图像特征。虽然 3D 纹理分析是计算机视觉中的一种关键方法，但它在 HR-pQCT 研究中很少被应用。受 HR-pQCT 扫描的高各向同性空间分辨率和信息密度的启发，我们开发并评估了一种后处理算法，通过 HR-pQCT 扫描中的纹理特征来量化微结构特征。在应用聚类算法对从每个小梁骨体素提取的纹理特征进行训练的阶段，确定了三个不同的聚类，即小梁微结构类（TMAC）。这些 TMAC 代表具有共同纹理特征的小梁骨区域。然后，将 TMAC 用于将新数据的体素自动分割成与训练的聚类特征相对应的三个区域。通过对两个 HR-pQCT 系统扫描的 14 个超远端桡骨样本进行评估，使用相对覆盖每个聚类的小梁骨体积的直方图来描述区域小梁骨纹理。我们通过评估该方法在两个 HR-pQCT 系统上的精度误差（PE）、组内相关系数（ICC）和骰子系数（DC），来评估该方法的同机和异机可重复性。在同机设置中，DC 显示出很好的可重复性，均值为 0.870±0.027（无单位）。即使在异机设置中，ICC 也显示出很高的可重复性，范围从 0.814 到 0.964。在初步的临床测试应用中，当区分绝经后妇女（n=18）和没有（n=18）脆性骨折的妇女时，TMAC 直方图似乎是一个很好的指标。总之，我们可以证明 3D 纹理分析和特征聚类似乎是一种很有前途的新 HR-pQCT 后处理工具，具有很好的可重复性，即使在两台不同的扫描仪之间也是如此。
