Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom.
PLoS One. 2012;7(12):e52075. doi: 10.1371/journal.pone.0052075. Epub 2012 Dec 20.
In 2001, Krueger and Glover introduced a model describing the temporal SNR (tSNR) of an EPI time series as a function of image SNR (SNR(0)). This model has been used to study physiological noise in fMRI, to optimize fMRI acquisition parameters, and to estimate maximum attainable tSNR for a given set of MR image acquisition and processing parameters. In its current form, this noise model requires the accurate estimation of image SNR. For multi-channel receiver coils, this is not straightforward because it requires export and reconstruction of large amounts of k-space raw data and detailed, custom-made image reconstruction methods. Here we present a simple extension to the model that allows characterization of the temporal noise properties of EPI time series acquired with multi-channel receiver coils, and reconstructed with standard root-sum-of-squares combination, without the need for raw data or custom-made image reconstruction. The proposed extended model includes an additional parameter κ which reflects the impact of noise correlations between receiver channels on the data and scales an apparent image SNR (SNR'(0)) measured directly from root-sum-of-squares reconstructed magnitude images so that κ = SNR'(0)/SNR(0) (under the condition of SNR(0)>50 and number of channels ≤32). Using Monte Carlo simulations we show that the extended model parameters can be estimated with high accuracy. The estimation of the parameter κ was validated using an independent measure of the actual SNR(0) for non-accelerated phantom data acquired at 3T with a 32-channel receiver coil. We also demonstrate that compared to the original model the extended model results in an improved fit to human task-free non-accelerated fMRI data acquired at 7T with a 24-channel receiver coil. In particular, the extended model improves the prediction of low to medium tSNR values and so can play an important role in the optimization of high-resolution fMRI experiments at lower SNR levels.
2001 年，Krueger 和 Glover 引入了一个模型，该模型将 EPI 时间序列的时间 SNR（tSNR）描述为图像 SNR（SNR(0)）的函数。该模型已被用于研究 fMRI 中的生理噪声，优化 fMRI 采集参数，并估计给定一组 MR 图像采集和处理参数下的最大可达到 tSNR。在其当前形式中，该噪声模型需要准确估计图像 SNR。对于多通道接收线圈，这并不简单，因为它需要导出和重建大量的 k 空间原始数据，并使用详细的、定制的图像重建方法。在这里，我们提出了对该模型的一个简单扩展，该扩展允许对使用多通道接收线圈采集并使用标准根和平方和组合重建的 EPI 时间序列的时间噪声特性进行特征描述，而无需原始数据或定制的图像重建。所提出的扩展模型包括一个附加参数 κ，该参数反映了噪声在接收通道之间的相关性对数据的影响，并对直接从根和平方和重建的幅度图像中测量的表观图像 SNR（SNR'(0)）进行缩放，使得 κ=SNR'(0)/SNR(0)（在 SNR(0)>50 且通道数≤32 的条件下）。使用蒙特卡罗模拟，我们表明可以非常准确地估计扩展模型参数。κ 的估计使用 3T 下使用 32 通道接收线圈采集的非加速体模数据的实际 SNR(0)的独立测量进行了验证。我们还表明，与原始模型相比，扩展模型导致对 7T 下使用 24 通道接收线圈采集的人类无任务非加速 fMRI 数据的拟合得到改善。特别是，扩展模型提高了低到中等 tSNR 值的预测能力，因此在较低 SNR 水平下优化高分辨率 fMRI 实验中可以发挥重要作用。
