Improving Sensitivity and Specificity of Parametric MRI Assessment of Cartilage

提高软骨参数 MRI 评估的灵敏度和特异性

• 批准号: 7964089
• 负责人: Richard Spencer
• 金额: $ 40.41万
• 依托单位: NATIONAL INSTITUTE ON AGING
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7964089
• 关键词: Algorithms; Cartilage; Categories; Characteristics; Classification; Clinical; Cluster Analysis; Collagen; Data; Data Analyses; Data Set; Development; Diffusion; Exhibits; Fibrinogen; Goals; Hydration status; Imaging Techniques; Iris; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Measurement; Measures; Metric; Modality; Modeling; Multivariate Analysis; Nature; Outcome; Outcome Measure; Preparation; Process; Relative (related person); Sampling; Sensitivity and Specificity; Shapes; Signal Transduction; Specific qualifier value; Specificity; Tissues; Weight; Work; articular cartilage; base; imaging Segmentation; improved; in vivo; indexing; magnetic field; progesterone 11-hemisuccinate-(2-iodohistamine); success

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. Additional analysis has demonstrated that under severe degradation, the conventional univariate analysis based on comparison of sample values with category means provides reasonable sensitivity and specificity. However, with more subtle degradation, we have found that model-based classification using Gaussian clusters is substantially more effective for classification. The probabalistic nature of this analysis lends itself readily to fuzzy clustering as well. While out application has been to cartilage degradation, the approach is much more general and may be useful in materials classification in general with magnetic resonance. Further ongoing work is centered around development of support vector machine analysis of degraded cartilage.

MRI越来越多地用于评估软骨，其总体目标是将非侵入性测量与组织的实际生物物理状态相关联。 有多种可用的MR技术和图像对比机制，可以根据其在实验准备和临床环境中表征组织状态的能力进行评价：T2对组织水合作用、胶原含量和胶原相对于主磁场的方向敏感;扩散（D）对大分子含量和水合作用敏感; T1主要对PG含量敏感，dGEMRIC指数也是如此。 磁化转移（MT）研究主要反映胶原蛋白含量。 也进行了异源性研究，Na+信号强度对局部PG含量敏感。 所有这些测量在某些情况下都表现出实用性。 然而，所有这些都具有有限的特异性，在正常软骨和例如退化软骨中测量的值之间或在软骨的不同区域之间观察到大的重叠。 参数组合可以比单个参数更具体;已经应用了各种多参数方法，特别是图像分割。 一个强大的方法是k-means聚类。 在我们的应用程序中，集群质心计算的基础上的散点图相对于测量参数。 然后将数据点分配给具有更接近质心的聚类。 对于真实的、不完全聚类的数据，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。 确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必不可少的。 类似地，可以使用任意数量的独立结果变量进行聚类;上面举例说明了双参数情况。这些结果变量也可以从完全不同的模式中获得，例如结合FT-IRIS结果测量使用MRI。 基本k均值聚类算法的进一步扩展是模糊k均值，其中组织被指定为在指定程度上属于特定聚类。 当数据集没有分解成定义的聚类时，如在软骨分析中，这是特别有用的。 基本算法的附加扩展消除了预定义数据中聚类数量的要求。 这在目标是区分两个离散组（例如正常和退化的软骨）的实验情况下可能不需要。 然而，它可能是非常有用的，在现实的情况下，更微妙的组织质量分级。 最后，我们注意到，可以应用不同的距离度量，允许结果测量的相对权重。 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来自基于参数化聚类形状，大小和方向的聚类分析。 其他分析表明，在严重降解情况下，基于样本值与类别平均值比较的传统单变量分析提供了合理的灵敏度和特异性。 然而，随着更微妙的退化，我们发现使用高斯聚类的基于模型的分类对于分类来说更有效。这种分析的概率性质也很容易适用于模糊聚类。虽然我们的应用程序已被软骨降解，该方法是更普遍的，并可能是有用的材料分类一般与磁共振。 进一步正在进行的工作是围绕支持向量机分析退化软骨的发展。