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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >Structural Quantification of Cartilage Changes Using Statistical Parametric Mapping
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Structural Quantification of Cartilage Changes Using Statistical Parametric Mapping

机译：使用统计参数映射的软骨变化的结构量化

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摘要

The early detection of Osteoarthritis (OA) treatment efficacy requires monitoring of small changes in cartilage morphology. Current approaches rely in carefully monitoring global cartilage parameters. However, they are not very sensitive to the detection of focal morphological changes in cartilage structure. This work presents the use of the statistical parametric mapping (SPM) for the detection and quantification of changes in cartilage morphology. The SPM is computed by first registering the baseline and the follow-up three dimensional (3D) reconstructions of the cartilage tissue. Once the registration is complete, the thickness changes for every cartilage point is computed which is followed by a model based estimation of the variance of thickness error. The cartilage thickness change and the variance estimations are used to compute the z-score map. The map is used to visualize and quantify significant changes in cartilage thickness. The z-map quantification provides the area of significant changes, the associated volume of changes as well as the average thickness of cartilage loss. Furthermore, thickness change distributions functions are normalized to provide the probability distribution functions (PDF). The PDF can be used to understand and quantify the differences among different treatment groups. The performance of the approach on simulated data and real subject data will be presented.
机译：早期检测骨关节炎（OA）的治疗功效需要监测软骨形态的细微变化。当前的方法依赖于仔细监测全球软骨参数。但是，它们对检测软骨结构的局部形态变化不是很敏感。这项工作介绍了统计参数映射（SPM）用于检测和量化软骨形态变化的用途。通过首先注册基线和软骨组织的后续三维（3D）重建来计算SPM。配准完成后，将计算每个软骨点的厚度变化，然后进行基于模型的厚度误差方差估算。软骨厚度变化和方差估计用于计算z评分图。该图用于可视化和量化软骨厚度的重大变化。 z-map量化提供了显着变化的区域，相关的变化量以及平均软骨损失厚度。此外，将厚度变化分布函数归一化以提供概率分布函数（PDF）。 PDF可用于了解和量化不同治疗组之间的差异。将介绍该方法在模拟数据和真实主题数据上的性能。

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