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: 8552506
• 负责人: Richard Spencer
• 金额: $ 65.1万
• 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/8552506
• 关键词: Algorithms; Cartilage; Categories; Characteristics; Classification; Clinical; Cluster Analysis; Collagen; Data; Data Analyses; Data Set; Development; Diffusion; Exhibits; Goals; Hydration status; Imaging Techniques; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Measurement; Measures; Metric; Modality; Modeling; Multivariate Analysis; Nature; Outcome; Outcome Measure; Preparation; Probability; Process; Relative (related person); Sampling; Sensitivity and Specificity; Shapes; Signal Transduction; Specific qualifier value; Specificity; Tissues; Training; Validation; Weight; Work; articular cartilage; base; imaging Segmentation; improved; in vivo; indexing; magnetic field; 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 available MR techniques and image contrast mechanisms that 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 our application has been to cartilage degradation, the approach is much more general and may be useful in materials classification in general with magnetic resonance. Current work is centered around development of support vector machine analysis of degraded cartilage. We find that this SVM approach may have significant advantages, in particular through a minimization of the over-training potential that occurs when developing a model with a training set for use on validation samples. In addition, the SVM, like the Gaussian clustering approach, lends itself to a graded assessment of cartilage degradation. This is through a sigmoidal probability function of the distance of a sample in parameter space from the decision hypersurface.

MRI 越来越多地用于评估软骨，总体目标是将无创测量与组织的实际生物物理状态联系起来。 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估：T2 对组织水合、胶原蛋白含量和胶原蛋白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。 还进行了异核研究，其中 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，在正常软骨和例如软骨中测量的值之间观察到很大的重叠。退化的软骨，或不同区域的软骨之间。 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。 一种稳健的方法是 k 均值聚类。 在我们的应用程序中，簇质心是根据测量参数的散点图计算的。 然后将数据点分配给具有更接近质心的簇。 真实的、不完全聚类的数据会存在一定数量的错误分类，但分析预计会比单变量分类更加准确。 决定算法成功的因素之一是测量参数的独立程度，因此仔细选择这些参数至关重要。 同样，可以使用任意数量的独立结果变量进行聚类；上面说明了双参数的情况。这些结果变量也可以来自完全不同的方式，例如将 MRI 与 FT-IRIS 结果测量结合使用。 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。 当数据集没有分解为定义的簇时（如软骨分析），这特别有用。 基本算法的额外扩展消除了预先定义数据中簇数量的要求。 在目标是区分两个离散组（例如正常软骨和退化软骨）的实验情况下，这可能不需要。 然而，它在具有更微妙的组织质量等级的现实情况中可能非常有用。 最后，我们注意到可以应用不同的距离度量，从而允许结果度量的相对权重。 我们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簇形状、大小和方向的聚类分析。 额外的分析表明，在严重降解的情况下，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要有效得多。这种分析的概率性质也很容易进行模糊聚类。虽然我们的应用是软骨降解，但该方法更为通用，并且可能在一般使用磁共振的材料分类中有用。 目前的工作集中在退化软骨支持向量机分析的开发上。 我们发现这种 SVM 方法可能具有显着的优势，特别是通过最大限度地减少在开发具有用于验证样本的训练集的模型时发生的过度训练潜力。 此外，SVM 与高斯聚类方法一样，适合对软骨退化进行分级评估。 这是通过参数空间中的样本与决策超曲面的距离的 S 形概率函数实现的。