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化学交换饱和转移 Z 谱中无关磁化强度的时域去除。

Time domain removal of irrelevant magnetization in chemical exchange saturation transfer Z-spectra.

机构信息

Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

出版信息

Magn Reson Med. 2013 Aug;70(2):547-55. doi: 10.1002/mrm.24812. Epub 2013 Jun 24.

Abstract

PURPOSE

To evaluate the possibility of processing Z-spectra using time domain analysis.

METHODS

An inverse Fourier transform (IFT) is applied on Z-spectra, thus transforming the chemical exchange saturation transfer (CEST) data into the time domain. Here, large interfering signals from solvent and semisolid magnetization transfer can be fit and filtered out. The method is demonstrated on a range of phantoms (creatine, a para-CEST agent, and hen egg white) and also in vivo on a mouse brain.

RESULTS

Using time domain analysis, signal components in Z-spectra could be fit very well, thus enabling irreverent or nuisance components to be removed. The method worked equally well for samples in a solution or a gel where the large contribution from conventional magnetization transfer contrast (MTC) was easily separated out. Results from egg white and mouse brain in vivo data showed that the large water resonance could easily be removed thus allowing the remaining signal to be analyzed without interference from direct water saturation.

CONCLUSIONS

This method successfully filtered out the large nuisance signals from bulk water and MTC in Z-spectra in a large variety of phantom types and also in vivo. It is expected to be a potentially powerful tool for CEST studies without needing asymmetry analysis.

摘要

目的

评估使用时域分析处理 Z 谱的可能性。

方法

对 Z 谱进行傅里叶逆变换(IFT),从而将化学交换饱和转移(CEST)数据转换为时域。在此,可以拟合和滤除溶剂和半固态磁化传递的大干扰信号。该方法在一系列模型(肌酸、对 CEST 试剂和鸡蛋蛋白)以及在小鼠大脑中的体内进行了验证。

结果

使用时域分析,可以很好地拟合 Z 谱中的信号分量,从而去除不可逆或干扰性的分量。该方法在溶液或凝胶中的样品中同样有效,其中常规磁化传递对比(MTC)的大贡献很容易分离出来。来自鸡蛋蛋白和体内小鼠大脑的数据结果表明,很容易去除大的水共振,从而可以在不受直接水饱和干扰的情况下分析剩余信号。

结论

该方法成功地从 Z 谱中的大量模型类型和体内的 bulk water 和 MTC 中过滤出了大的干扰信号。它有望成为一种强大的 CEST 研究工具,而无需进行不对称性分析。

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