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首页> 外文期刊>Applied Magnetic Resonance >Bayesian Modeling of NMR Data: Quantifying Longitudinal Relaxation in Vivo, and in Vitro with a Tissue-Water-Relaxation Mimic (Crosslinked Bovine Serum Albumin)
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Bayesian Modeling of NMR Data: Quantifying Longitudinal Relaxation in Vivo, and in Vitro with a Tissue-Water-Relaxation Mimic (Crosslinked Bovine Serum Albumin)

机译：NMR数据的贝叶斯建模：量化体内纵向松弛，并用组织 - 水松弛模拟（交联牛血清白蛋白）

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摘要

Recently, a number of magnetic resonance imaging protocols have been reported that seek to exploit the effect of dissolved oxygen (O-2, paramagnetic) on the longitudinal H-1 relaxation of tissue water, thus providing image contrast related to tissue oxygen content. However, tissue water relaxation is dependent on a number of mechanisms and this raises the issue of how best to model the relaxation data. This problem, the model selection problem, occurs in many branches of science and is optimally addressed by Bayesian probability theory. High signal-to-noise, densely sampled, longitudinal H-1 relaxation data were acquired from rat brain in vivo and from a cross-linked bovine serum albumin (xBSA) phantom, a sample that recapitulates the relaxation characteristics of tissue water in vivo. Bayesian-based model selection was applied to a cohort of five competing relaxation models: (1) monoexponential, (2) stretched-exponential, (3) biexponential, (4) Gaussian (normal) R (1)-distribution, and (5) gamma R (1)-distribution. Bayesian joint analysis of multiple replicate datasets revealed that water relaxation of both the xBSA phantom and in vivo rat brain was best described by a biexponential model, while xBSA relaxation datasets truncated to remove evidence of the fast relaxation component were best modeled as a stretched exponential. In all cases, estimated model parameters were compared to the commonly used monoexponential model. Reducing the sampling density of the relaxation data and adding Gaussian-distributed noise served to simulate cases in which the data are acquisition-time or signal-to-noise restricted, respectively. As expected, reducing either the number of data points or the signal-to-noise increases the uncertainty in estimated parameters and, ultimately, reduces support for more complex relaxation models.
机译：最近，已经报道了许多磁共振成像协议，以便利用溶解的氧（O-2，paramagnetic）对组织水的纵向H-1弛豫的影响，从而提供与组织氧含量相关的图像造影。然而，组织水松弛取决于许多机制，这提出了如何最好地建模放松数据的问题。这个问题是模型选择问题，发生在许多科学分支中，并由贝叶斯概率理论最佳地解决。从体内大鼠脑中和来自交联牛血清白蛋白（XBSA）幻影的大鼠脑中获取高信噪比，纵向H-1弛豫数据，该样品重新承载体内组织水的松弛特性。基于贝叶斯的模型选择适用于五个竞争放松模型的队列：（1）单百分比，（2）拉伸指数，（3）Biexponential，（4）高斯（正常）R（1） - （5 ）Gamma R（1） - 分布。多重复制数据集的贝叶斯联合分析显示，XBSA幻影和体内大鼠体内的水松弛是由Biexponential模型描述的，而截断以去除快速松弛组分的证据的XBSA松弛数据集最好是作为拉伸指数建模的。在所有情况下，将估计的模型参数与常用的单展模型进行比较。降低放松数据的采样密度并添加高斯分布式噪声，以模拟数据是获取时间或信噪比的情况。如预期的那样，减少数据点的数量或信号 - 噪声增加了估计参数的不确定性，并且最终会降低对更复杂的放松模型的支持。

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