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Coil combination of multichannel MRSI data at 7 T: MUSICAL

机译:7 T时多通道MRSI数据的线圈组合:MUSICAL

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摘要

The goal of this study was to evaluate a new method of combining multi-channel 1H MRSI data by direct use of a matching imaging scan as a reference, rather than computing sensitivity maps. Seven healthy volunteers were measured on a 7-T MR scanner using a head coil with a 32-channel array coil for receive-only and a volume coil for receive/transmit. The accuracy of prediction of the phase of the 1H MRSI data with a fast imaging pre-scan was investigated with the volume coil. The array coil 1H MRSI data were combined using matching imaging data as coil combination weights. The signal-to-noise ratio (SNR), spectral quality, metabolic map quality and Cramér–Rao lower bounds were then compared with the data obtained by two standard methods, i.e. using sensitivity maps and the first free induction decay (FID) data point. Additional noise decorrelation was performed to further optimize the SNR gain. The new combination method improved significantly the SNR (+29%), overall spectral quality and visual appearance of metabolic maps, and lowered the Cramér–Rao lower bounds (−34%), compared with the combination method based on the first FID data point. The results were similar to those obtained by the combination method using sensitivity maps, but the new method increased the SNR slightly (+1.7%), decreased the algorithm complexity, required no reference coil and pre-phased all spectra correctly prior to spectral processing. Noise decorrelation further increased the SNR by 13%. The proposed method is a fast, robust and simple way to improve the coil combination in 1H MRSI of the human brain at 7 T, and could be extended to other 1H MRSI techniques. © 2013 The Authors. NMR in Biomedicine published by John Wiley & Sons, Ltd.
机译:本研究的目的是通过直接使用匹配的成像扫描作为参考,而不是计算灵敏度图,来评估结合多通道 1 H MRSI数据的新方法。在7-T MR扫描仪上对7名健康志愿者进行了测量,其头部线圈带有一个32通道阵列线圈(仅用于接收)和一个体积线圈(用于接收/发送)。利用体积线圈研究了快速成像预扫描对 1 H MRSI数据的相位预测的准确性。使用匹配的成像数据作为线圈组合权重,对阵列线圈 1 H MRSI数据进行组合。然后将信噪比(SNR),光谱质量,代谢图质量和Cramér-Rao下限与通过两种标准方法获得的数据进行比较,即使用灵敏度图和第一个自由感应衰减(FID)数据点。进行了额外的噪声去相关,以进一步优化SNR增益。与基于第一个FID数据点的组合方法相比,新的组合方法显着提高了SNR(+ 29%),整体谱图质量和代谢图的视觉外观,并降低了Cramér-Rao下界(−34%) 。结果与使用灵敏度图的组合方法获得的结果相似,但是新方法将SNR略微提高(+ 1.7%),降低了算法复杂度,不需要参考线圈,并且在进行光谱处理之前正确地对所有光谱进行了预相位调整。噪声去相关进一步将SNR提高了13%。该方法是一种改善人脑 1 H MRSI在7 T时线圈组合的快速,鲁棒和简单的方法,可以扩展到其他 1 H MRSI技术。 ©2013作者。约翰·威利父子公司(John Wiley&Sons,Ltd.)出版的《生物医学中的NMR》。

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