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Rician Distributed FMRI: Asymptotic Power Analysis and Cramér–Rao Lower Bounds

机译:Rician分布式FMRI:渐近幂分析和Cramér-Rao下界

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Traditional functional MRI detection statistics for activation and hemodynamic response modeling assume Gaussian data, which is true only for high signal-to-noise ratio (SNR). The correct distribution is Rician. In this correspondence, we provide two new developments in the Rician case. First, a derivation of the theoretical asymptotic power function (as the number of samples goes to infinity). Second, the derivation of a Cramér–Rao lower bound. This allows a correct assessment of the impact of various signal and noise levels on detection power for activation and/or hemodynamic response parameter estimation accuracy. Based on our analysis, we are able to extend existing definitions of SNR by considering variation not only in baseline but also in drifts.
机译:用于激活和血流动力学反应建模的传统功能性MRI检测统计数据采用高斯数据,只有高信噪比(SNR)时才如此。正确的分布是Rician。在此通信中,我们为Rician案提供了两个新的发展。首先,推导理论渐近幂函数(当样本数达到无穷大时)。第二,推导Cramér-Rao下界。这允许针对激活和/或血液动力学响应参数估计精度的各种信号和噪声水平对检测功率的影响的正确评估。基于我们的分析,我们能够通过不仅考虑基线而且考虑漂移的变化来扩展SNR的现有定义。

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