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基于高空间分辨率磁共振图像的小梁骨微结构的测地线拓扑分析。

Geodesic topological analysis of trabecular bone microarchitecture from high-spatial resolution magnetic resonance images.

作者信息

Carballido-Gamio Julio, Krug Roland, Huber Markus B, Hyun Ben, Eckstein Felix, Majumdar Sharmila, Link Thomas M

机构信息

Musculoskeletal and Quantitative Imaging Research Group, Department of Radiology, University of California, San Francisco, San Francisco, California 94158, USA.

出版信息

Magn Reson Med. 2009 Feb;61(2):448-56. doi: 10.1002/mrm.21835.

Abstract

In vivo assessment of trabecular bone microarchitecture could improve the prediction of fracture risk and the efficacy of osteoporosis treatment and prevention. Geodesic topological analysis (GTA) is introduced as a novel technique to quantify the trabecular bone microarchitecture from high-spatial resolution magnetic resonance (MR) images. Trabecular bone parameters that quantify the scale, topology, and anisotropy of the trabecular bone network in terms of its junctions are the result of GTA. The reproducibility of GTA was tested with in vivo images of human distal tibiae and radii (n = 6) at 1.5 Tesla; and its ability to discriminate between subjects with and without vertebral fracture was assessed with ex vivo images of human calcanei at 1.5 and 3.0 Tesla (n = 30). GTA parameters yielded an average reproducibility of 4.8%, and their individual areas under the curve (AUC) of the receiver operating characteristic curve analysis for fracture discrimination performed better at 3.0 than at 1.5 Tesla reaching values of up to 0.78 (p < 0.001). Logistic regression analysis demonstrated that fracture discrimination was improved by combining GTA parameters, and that GTA combined with bone mineral density (BMD) allow for better discrimination than BMD alone (AUC = 0.95; p < 0.001). Results indicate that GTA can substantially contribute in studies of osteoporosis involving imaging of the trabecular bone microarchitecture.

摘要

对小梁骨微结构进行体内评估可改善骨折风险预测以及骨质疏松症治疗和预防的效果。引入了测地线拓扑分析(GTA)作为一种从高空间分辨率磁共振(MR)图像量化小梁骨微结构的新技术。基于小梁骨网络的连接点来量化其尺度、拓扑结构和各向异性的小梁骨参数是GTA的结果。在1.5特斯拉条件下,利用人体胫骨远端和桡骨的体内图像(n = 6)测试了GTA的可重复性;并在1.5和3.0特斯拉条件下,利用人体跟骨的离体图像(n = 30)评估了其区分有和无椎体骨折受试者的能力。GTA参数的平均可重复性为4.8%,其用于骨折鉴别的受试者操作特征曲线分析的各个曲线下面积(AUC)在3.0特斯拉时比在1.5特斯拉时表现更好,最高可达0.78(p < 0.001)。逻辑回归分析表明,通过组合GTA参数可改善骨折鉴别,并且GTA与骨密度(BMD)相结合比单独使用BMD能实现更好的鉴别(AUC = 0.95;p < 0.001)。结果表明,GTA在涉及小梁骨微结构成像的骨质疏松症研究中可做出重大贡献。

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