Cavassila S, Deval S, Huegen C, Van Ormondt D, Graveron-Demilly D
Laboratoire de RMN, CNRS UPRESA 5012, UCB LYON I-CPE, Villeurbanne, France.
Invest Radiol. 1999 Mar;34(3):242-6. doi: 10.1097/00004424-199903000-00015.
This work concerns quantitation of in vivo magnetic resonance spectroscopy signals and the influence of prior knowledge on the precision of parameter estimates. The authors point out how prior knowledge can be used for experiments.
The Cramer-Rao lower bounds formulae of the noise-related standard deviations on spectral parameters for doublets and triplets were derived. Chemical prior knowledge of the multiplet structures was used.
The benefit of chemical prior knowledge was estimated for doublet and triplet structures of arbitrary shape. Then, it was used to quantify in vivo 31P time-series signals of rat brain.
Analytic expressions of errors on parameter estimates were derived, enabling prediction of the benefit of prior knowledge on quantitation results. These formulae allow us to state, for a given noise level, if the quantitation of strongly overlapping peaks such as adenosine triphosphate multiplets can be performed successfully.
本研究涉及体内磁共振波谱信号的定量分析以及先验知识对参数估计精度的影响。作者指出了先验知识在实验中的应用方式。
推导了与噪声相关的双峰和三峰光谱参数标准偏差的克拉美-罗下界公式。利用了多重峰结构的化学先验知识。
评估了任意形状双峰和三峰结构的化学先验知识的益处。然后,将其用于量化大鼠脑内的体内31P时间序列信号。
推导了参数估计误差的解析表达式,能够预测先验知识对定量结果的益处。这些公式使我们能够针对给定的噪声水平,判断诸如三磷酸腺苷多重峰等强重叠峰的定量分析是否能够成功进行。