Suppr超能文献

基于增强程序和线性预测方法的自动体内核磁共振数据处理

Automatic in vivo NMR data processing based on an enhancement procedure and linear prediction method.

作者信息

Diop A, Briguet A, Graveron-Demilly D

机构信息

Laboratoire de RMN, Université Claude Bernard, Lyon I, Villeurbanne, France.

出版信息

Magn Reson Med. 1992 Oct;27(2):318-28. doi: 10.1002/mrm.1910270211.

Abstract

A new data processing method for in vivo NMR data quantitation is presented. This method (EPLPSVD) is based on the enhancement procedure (EP) proposed by J. A. Cadzow (IEEE Trans. Acoust. Speech Signal Process. 36, 49, 1988) followed by the usual linear prediction method using the singular value decomposition (LPSVD). The evaluation of this protocol is performed using synthesized 31P signals with different signal-to-noise ratios. A Monte-Carlo simulation as a function of signal-to-noise ratio (SNR) has proved that EPLPSVD leads to unbiased estimated values of parameters. Then the Cramer-Rao method yields reliable confidence intervals for the estimated parameters. The estimates of NMR parameters using EPLPSVD are reliable and accurate for SNR > or = 1.2 while the LPSVD method failed for SNR < or = 4. This protocol is applied to analyze automatically a series of 31P free induction decays obtained from the human gastrocnemius muscle during exercise. Spectral parameters with their confidence intervals, curves of relative intensity variations in phosphocreatine and inorganic phosphate, and pH curves are automatically provided.

摘要

提出了一种用于体内核磁共振(NMR)数据定量分析的新数据处理方法。该方法(EPLPSVD)基于J. A. Cadzow提出的增强程序(EP)(《IEEE声学、语音和信号处理汇刊》36卷,49页,1988年),随后采用基于奇异值分解的常规线性预测方法(LPSVD)。使用具有不同信噪比的合成31P信号对该方案进行评估。作为信噪比(SNR)函数的蒙特卡罗模拟证明,EPLPSVD能得出无偏参数估计值。然后,克拉美-罗方法为估计参数产生可靠的置信区间。当SNR≥1.2时,使用EPLPSVD对NMR参数的估计是可靠且准确的,而当SNR≤4时,LPSVD方法失效。该方案用于自动分析运动过程中从人体腓肠肌获得的一系列31P自由感应衰减。自动提供带有置信区间的光谱参数、磷酸肌酸和无机磷酸盐相对强度变化曲线以及pH曲线。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验