Athinoula A. Martinos Imaging Center at McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Neuroimage. 2011 Mar 15;55(2):597-606. doi: 10.1016/j.neuroimage.2010.11.084. Epub 2010 Dec 16.
Sensitivity in BOLD fMRI is characterized by the signal to noise ratio (SNR) of the time-series (tSNR), which contains fluctuations from thermal and physiological noise sources. Alteration of an acquisition parameter can affect the tSNR differently depending on the relative magnitude of the physiological and thermal noise, therefore knowledge of this ratio is essential for optimizing fMRI acquisitions. In this study, we compare image and time-series SNR from array coils at 3T with and without parallel imaging (GRAPPA) as a function of image resolution and acceleration. We use the "absolute unit" SNR method of Kellman and McVeigh to calculate the image SNR (SNR(0)) in a way that renders it comparable to tSNR, allowing determination of the thermal to physiological noise ratio, and the pseudo-multiple replica method to quantify the image noise alterations due to the GRAPPA reconstruction. The Kruger and Glover noise model, in which the physiological noise standard deviation is proportional to signal strength, was found to hold for the accelerated and non-accelerated array coil data. Thermal noise dominated the EPI time-series for medium to large voxel sizes for single-channel and 12-channel head coil configurations, but physiological noise dominated the 32-channel array acquisition even at 1 mm × 1mm × 3 mm resolution. At higher acceleration factors, image SNR is reduced and the time-series becomes increasingly thermal noise dominant. However, the tSNR reduction is smaller than the reduction in image SNR due to the presence of physiological noise.
BOLD fMRI 的灵敏度由时间序列（tSNR）的信噪比（SNR）来表征，其中包含了来自热和生理噪声源的波动。采集参数的改变会根据生理和热噪声的相对大小，对 tSNR 产生不同的影响，因此了解这个比值对于优化 fMRI 采集是至关重要的。在这项研究中，我们比较了 3T 下带有和不带有并行成像（GRAPPA）的阵列线圈的图像和时间序列 SNR，作为图像分辨率和加速的函数。我们使用 Kellman 和 McVeigh 的“绝对单位” SNR 方法来计算图像 SNR（SNR(0)），使得它与 tSNR 可比，从而可以确定热到生理噪声的比值，并使用伪多复本方法来量化由于 GRAPPA 重建而导致的图像噪声变化。Kruger 和 Glover 噪声模型表明，生理噪声的标准差与信号强度成正比，这一模型适用于加速和非加速的阵列线圈数据。对于单通道和 12 通道头部线圈配置，对于中等到较大的体素大小，热噪声主导了 EPI 时间序列，但即使在 1mm×1mm×3mm 的分辨率下，生理噪声也主导了 32 通道阵列采集。在更高的加速因子下，图像 SNR 降低，时间序列变得越来越热噪声主导。然而，由于生理噪声的存在，tSNR 的降低小于图像 SNR 的降低。
