Improving Sensitivity and Specificity of Parametric MRI Assessment of Cartilage

MRI越来越多地被用于评估软骨，总的目标是将非侵入性测量与组织的实际生物物理状态联系起来。有各种可用的MR技术和图像对比机制，可以根据它们表征组织状态的能力进行评估，无论是在实验准备中还是在临床环境中：T2对组织水合、胶原含量和相对于主磁场的胶原取向敏感；扩散(D)对大分子含量和水合敏感；T1主要对PG含量敏感，dGEMRIC指数也是如此。磁化传递(MT)研究主要反映胶原含量。还进行了异核研究，Na+信号强度对局部PG含量很敏感。所有这些测量在某些情况下都显示出实用性。然而，所有这些都具有有限的特异性，在正常软骨和退化软骨的测量值之间或软骨的不同区域之间观察到很大的重叠。 参数组合可以比单个参数更具体；各种多参数方法已经被应用，特别是在图像分割中。一种稳健的方法是k-均值聚类。在我们的应用中，基于相对于测量参数的散点图来计算集群质心。然后，将数据点分配给质心较近的集群。对于真实的、不完全聚集的数据，将会有一定数量的错误分类，但预计分析将比单变量分类准确得多。 决定算法成功的一个因素是测量参数的独立程度，因此仔细选择这些参数是至关重要的。类似地，可以使用任意数量的独立结果变量进行分组；上面说明了双参数的情况。这些结果变量也可以从完全不同的模式中得出，例如结合使用MRI和FT-IRIS结果测量。基本k-均值聚类算法的进一步扩展是模糊k-均值，其中组织被指定为属于特定程度的特定聚类。当数据集没有分解成定义的簇时，这是特别有用的，例如在软骨分析中。对基本算法的附加扩展消除了预先定义数据内的簇数的要求。在实验情况下，这可能不是必需的，因为在实验情况下，目标是区分两个不同的组，例如正常和退化的软骨。然而，在组织质量具有更细微渐变的现实情况下，它可能非常有用。最后，我们注意到可以应用不同的距离度量，从而允许对结果度量进行相对加权。 我们已经尝试了多种方法来解决这个问题，迄今为止最好的结果是基于参数化的簇形状、大小和方向进行的簇分析。

MRI is increasingly used to assess cartilage, with the overall goal of relating noninvasive measurements to the actual biophysical status of the tissue. There are a variety of MR techniques and image contrast mechanisms available which can be evaluated in terms of their ability to characterize tissue status, both in experimental preparations and in the clinical setting: T2 is sensitive to tissue hydration, collagen content, and collagen orientation with respect to the main magnetic field; diffusion (D) is sensitive to macromolecular content and hydration; T1 is sensitive primarily to PG content, as is the dGEMRIC index. Magnetization transfer (MT) studies primarily reflect collagen content. Heteronuclear studies have also been performed, with the Na+ signal intensity being sensitive to local PG content. All of these measurements exhibit utility in certain circumstances. However, all are of limited specificity, with a large overlap observed between values measured in normal cartilage and e.g. degraded cartilage, or between different regions of cartilage. Parameter combinations can be more specific than single parameters; a variety of multi-parametric approaches have been applied, particularly to image segmentation. A robust approach is k-means clustering. In our application, cluster centroids are calculated based on a scatterplot with respect to measured parameters. A data point is then assigned to the cluster with the closer centroid. There will be a certain number of misclassifications with real, imperfectly clustered, data, but the analysis is expected to be substantially more accurate than univariate classifications. One factor determining the success of the algorithm is the degree of independence of the measured parameters, so that careful selection of these is essential. Similarly, clustering can be performed with any number of independent outcome variables; the two-parameter case was illustrated above. These outcome variables can also be derived from entirely different modalities, such as use of MRI in conjunction with FT-IRIS outcome measures. A further extension of the basic k-means clustering algorithm is fuzzy k-means, where tissue is designated as belonging to a particular cluster to a specified degree. This is of particular utility when the dataset does not break into defined clusters, as in cartilage analysis. An additional extension of the basic algorithm removes the requirement for pre-defining the number of clusters within the data. This may not be required in experimental situations in which the goal is to distinguish two discrete groups, such as normal and degraded cartilage. However, it may be very useful in realistic situations with more subtle gradations of tissue quality. Finally, we note that different distance metrics may be applied, permitting relative weighting of the outcome measures. We have tried multiple approaches to this problem, with the best results to date resulting from a cluster analysis based on parameterized cluster shapes, sizes, and orientations.