Multicompartment quantification of tissue in vitro and in vivo with magnetic resonance imaging and spectroscopy

利用磁共振成像和光谱学对体外和体内组织进行多室定量

• 批准号: 10252565
• 负责人: Richard Spencer
• 金额: $ 1.63万
• 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/10252565
• 关键词: Algorithms; Bayesian Analysis; Bayesian Method; Brain; Cartilage; Clinical; Data; Dimensions; Equilibrium; Human; Image; In Vitro; Knee; Least-Squares Analysis; Magnetic Resonance Imaging; Maps; Measures; Methods; Modeling; Myelin; Nature; Noise; Physiologic pulse; Probability; Proteoglycan; Residual state; Signal Transduction; Surface; Techniques; Time; Tissues; Water; base; experimental study; high dimensionality; human subject; improved; in vivo; myelination; novel; simulation; social exclusion; spectroscopic imaging; volunteer

Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) has been proposed as a rapid approach for multicomponent relaxometry. However, even for the simplest two-pool signal model consisting of myelin-associated and non-myelin-associated water, the dimensionality of the parameter space for obtaining MWF estimates remains high. This renders parameter estimation difficult, especially at low-to-moderate signal-to-noise ratios (SNRs), due to the presence of local minima and the flatness of the fit residual energy surface used for parameter determination using conventional nonlinear least squares (NLLS)-based algorithms. We have introduced Bayesian approaches for analysis of the mcDESPOT signal model to stabilize the analysis. Given the high-dimensional nature of the mcDESPOT signal model, and, therefore the high-dimensional marginalizations over nuisance parameters needed to derive the posterior probability distribution of the MWF, the Bayesian analyses introduced here use different approaches to reduce the dimensionality of the parameter space. The first approach uses normalization by average signal amplitude, and assumes that noise can be accurately estimated from signal-free regions of the image. The second approach likewise uses average amplitude normalization, but incorporates a full treatment of noise as an unknown variable through marginalization. The third approach does not use amplitude normalization and incorporates marginalization over both noise and signal amplitude. Through extensive Monte Carlo numerical simulations and analysis, we demonstrated markedly improved accuracy and precision in the estimation of MWF using these Bayesian methods as compared to the stochastic region contraction (SRC) implementation of NLLS. These methods are general and have been applied to mapping proteoglycan content in the human knee in vivo.

多组分驱动的平衡单脉冲观察T1和T2（McDespot）已被认为是用于多组分弛豫计的快速方法。但是，即使对于最简单的两池信号模型，由髓磷脂相关和非膜蛋白相关的水组成，获得MWF估计值的参数空间的维度仍然很高。由于存在局部最小值，并且使用基于常规的非线性最小二乘（NLL）的拟合剩余能量表面，因此这使参数估计变得困难，尤其是在低到中度信号噪声比（SNRS）（SNRS）（SNRS）的困难。 算法。 我们引入了贝叶斯方法，用于分析McDespot信号模型以稳定分析。鉴于McDespot信号模型的高维质，因此，与得出MWF后验概率分布所需的高维边缘化相比，此处引入的贝叶斯分析使用不同的方法使用不同的方法来降低参数参数空间的维度。第一种方法使用平均信号幅度的归一化，并假设噪声 可以从图像的无信号区域准确估算。第二种方法同样使用平均幅度归一化，但通过边缘化将噪声的完整处理作为未知变量。第三种方法不使用振幅归一化，并在噪声和信号振幅上都结合了边缘化。通过广泛的蒙特卡洛数值模拟和分析，我们证明了使用这些贝叶斯方法与NLL的随机区域收缩（SRC）相比，使用这些贝叶斯方法显着提高了MWF的准确性和精度。 这些方法是一般的，已应用于体内人膝盖中的蛋白聚糖含量。