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Forecasting the quality of water-suppressed (1) H MR spectra based on a single-shot water scan.

机译:基于单次水扫描,预测水抑制(1)H MR谱的质量。

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摘要

PURPOSE\ud\udTo investigate whether an initial non-water-suppressed acquisition that provides information about the signal-to-noise ratio (SNR) and linewidth is enough to forecast the maximally achievable final spectral quality and thus inform the operator whether the foreseen number of averages and achieved field homogeneity is adequate.\ud\udMETHODS\ud\udA large range of spectra with varying SNR and linewidth was simulated and fitted with popular fitting programs to determine the dependence of fitting errors on linewidth and SNR. A tool to forecast variance based on a single acquisition was developed and its performance evaluated on simulated and in vivo data obtained at 3 Tesla from various brain regions and acquisition settings.\ud\udRESULTS\ud\udA strong correlation to real uncertainties in estimated metabolite contents was found for the forecast values and the Cramer-Rao lower bounds obtained from the water-suppressed spectra.\ud\udCONCLUSION\ud\udIt appears to be possible to forecast the best-case errors associated with specific metabolites to be found in model fits of water-suppressed spectra based on a single water scan. Thus, nonspecialist operators will be able to judge ahead of time whether the planned acquisition can possibly be of sufficient quality to answer the targeted clinical question or whether it needs more averages or improved shimming. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine.
机译:目的\ ud \ ud调查提供信号信噪比(SNR)和线宽信息的初始非水抑制采集是否足以预测最大可达到的最终频谱质量,从而告知操作员是否可预见的数量\ ud \ udMETHODS \ ud \ ud模拟了具有变化的SNR和线宽的大范围光谱,并使用流行的拟合程序进行拟合,以确定拟合误差对线宽和SNR的依赖性。开发了一种基于单次采集预测方差的工具,并根据3特斯拉从不同大脑区域和采集设置获得的模拟和体内数据评估了其性能。\ ud \ udRESULTS \ ud \ ud与估计代谢物的实际不确定性密切相关找到了预测值的内容以及从水抑制谱获得的Cramer-Rao下限。\ ud \ ud结论\ ud \ ud似乎有可能预测与模型中发现的特定代谢物相关的最佳情况错误基于单次水扫描的水抑制光谱拟合。因此,非专业操作人员将能够提前判断计划的采集是否可能具有足够的质量来回答目标临床问题,或者是否需要更多的平均值或改善的匀场。 Magn Reson Med,2016年。©2016国际磁共振医学学会。

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