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.

多分量驱动平衡单脉冲观察T1和T2 （mcDESPOT）已被提出作为一种快速的多分量弛缓测量方法。然而，即使对于由髓磷脂相关和非髓磷脂相关水组成的最简单的双池信号模型，用于获得MWF估计的参数空间的维数仍然很高。这使得参数估计变得困难，特别是在低至中等信噪比（SNRs）下，由于存在局部极小值和使用传统的非线性最小二乘（NLLS）进行参数确定的拟合剩余能量表面的平坦性