INTER-SITE QA DEVELOPMENT FOR THE FIRST BIRN

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 FIRST（功能成像研究精神分裂症测试床）生物医学信息学研究网络（fBIRN）计划是有史以来第一个针对精神分裂症的大规模、多中心fMRI研究（http：www.nbirn.net）。fBIRN项目将汇集来自11个参与站点的fMRI数据，以便在适当的时间内获得大量多样化的研究人群。为了保持扫描仪的稳定性，我们的中心开发了在所有研究中心使用的定期质量保证（QA）方法。在每个站点定期进行扫描，并将数据上传到网络服务器。编写了一份摘要，并被ISMRM 2004接受。 对于纵向fMRI研究，定期监测扫描仪性能以确保稳定性、几何精度、信噪比（SNR）和其他特征保持准确和一致是很重要的。当来自多个扫描仪的数据在最初和较长时间内进行比较或组合时，例如在fBIRN项目中，恒定性就更加重要。我们中心开发的QA方法适用于fBIRN。其中最重要的是检查扫描仪的稳定性，但也推导出信噪比，并在这里描述。 数据采集每周稳定性测试使用fBIRN fMRI扫描方案，使用直径为17 cm的琼脂体模（35个轴向，4 mm连续切片，22 cm FOV，64 x 64矩阵，TE 30 ms/40 ms（3 T-4 T/1.5T），TR 3000 ms，200个时间帧，10分钟扫描时间，EPI或螺旋）。琼脂糖凝胶体模掺杂NaCl，以呈现头部典型的RF负载，并且优于水，以避免漩涡伪影。 分析只有来自中央切片（18）的时间序列数据用于分析。使用图像中心的20 x20 ROI测量系列中每个时间帧的图像强度。在二次去趋势后计算RMS分数波动，并从趋势线的极值获得漂移（图1，顶部）。计算傅立叶光谱（图1，中间）。如图1底部所示，使用尺寸从1x 1到20 x20变化的ROI进行“Weisskoff分析”。在此分析中，标准差（STD）应减少为？（ROI中的体素数量），如果噪声不相关。来自RF或梯度源的系统不稳定性倾向于引起低空间频率图像波动，其表现为体素之间的互相关。这又导致STD偏离对1/（ROI宽度）的线性依赖性，并且随着ROI尺寸变得更大而达到下限，如图1所示（参见图1）。用虚线表示）。请注意，上图是使用最大的ROI大小执行的，有意选择以表示系统限制（而不是SNR限制）的噪声。此外，信号与波动噪声比（SFNR）由（时间序列平均图像）/（时间序列标准偏差图像）制成的图中的ROI测量。最后，从通过减去偶数时间帧的平均值和奇数时间帧的平均值并除以平均信号而获得的噪声图像中的利用ROI测量的噪声来计算SNR。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The FIRST (Functional Imaging Research Schizophrenia Testbed) Biomedical Informatics Research Network (fBIRN) program is the first-ever large scale, multi-center fMRI study of schizophrenia (http://www.nbirn.net). The fBIRN project will pool fMRI data from each of the 11 participating sites to enable the acquisition of a large and diverse study population in a modest time period. To maintain scanner stability, our Center developed methods in use at all the sites for periodic quality assurance (QA). Scans are performed regularly at each of the sites and data uploaded to the network servers. An abstract was prepared and accepted for ISMRM 2004. For longitudinal fMRI studies, it is important to periodically monitor scanner performance to assure that stability, geometric accuracy, signal to noise ratio (SNR) and other characteristics remain accurate and consistent. Constancy is even more important when data from multiple scanners are being compared or combined both initially and over extended periods of time, such as in the fBIRN project. QA methods developed at our Center were adapted for fBIRN. The most important of these examines scanner stability but also derives SNR and is described here. Data Acquisition The weekly stability test uses the fBIRN fMRI scan protocol with a 17 cm diameter agar phantom (35 axial, 4 mm contiguous slices, 22 cm FOV, 64x64 matrix, TE 30ms/40ms (3T-4T/1.5T), TR 3000ms, 200 time frames, 10 minute scan time, EPI or spiral). The agarose gel phantom was doped with NaCl to present RF loading typical of a head and was preferred over water to avoid swirling artifacts. Analysis Only timeseries data from the central slice (18) is used for analysis. Image intensities are measured for each time frame in the series using a 20x20 ROI centered in the image. The RMS fractional fluctuation is calculated after quadratic detrending, and drift is obtained from extrema of the trend line (Fig. 1, top). The Fourier spectrum is calculated (Fig. 1, middle). A 'Weisskoff analysis' is performed using ROIs with sizes varying from 1x1 to 20x20, as shown in Fig. 1, bottom. In this analysis, the standard deviation (STD) should decrease as ?(number of voxels in ROI) if the noise is uncorrelated. System instabilities from RF or gradient sources tend to cause low spatial-frequency image fluctuations, which are manifested as cross-correlation between voxels. This in turn causes STD to depart from linear dependence on 1/(ROI width) and hit a floor as the ROI size becomes larger, as shown in Fig. 1 (cf. with dashed line). Note that the top plot is performed with the largest ROI size, intentionally picked to represent the system limited (rather than SNR limited) noise. In addition, the signal to fluctuation noise ratio (SFNR) is measured by an ROI in a map made from (timeseries mean image)/(timeseries standard deviation image). Finally, the SNR is calculated from noise measured with an ROI in the noise image obtained by subtracting the average of the even time frames and average of the odd time frames and dividing into the mean signal.