INTER-SITE QA DEVELOPMENT FOR THE FIRST BIRN

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 第一个(功能成像研究精神分裂症试验床)生物医学信息学研究网络计划是有史以来第一个大规模、多中心的精神分裂症功能磁共振研究(http://www.nbirn.net).FBIRN项目将汇集11个参与站点中每个站点的fMRI数据，以便在适当的时间段内获得大量和多样化的研究人群。为了保持扫描仪的稳定性，我们中心制定了在所有地点使用的定期质量保证(QA)方法。定期在每个站点执行扫描，并将数据上载到网络服务器。为2004年ISMRM编写并接受了一份摘要。 对于纵向功能磁共振成像研究，定期监测扫描仪性能非常重要，以确保稳定性、几何精度、信噪比(SNR)和其他特性保持准确和一致。当对来自多台扫描仪的数据进行初始和较长时间的比较或合并时，一致性甚至更加重要，例如在fBIRN项目中。我们中心开发的质量保证方法适用于fBIRN。其中最重要的是检查扫描仪的稳定性，但也得出SNR，并在这里描述。 数据采集每周稳定性测试使用fBIRN fMRI扫描方案和直径17 cm的琼脂模型(35轴，4 mm连续切片，22 cm FOV，64x64矩阵，TE 30ms/40ms(3T-4T/1.5T)，TR3000ms，200时间帧，10分钟扫描时间，EPI或螺旋)。琼脂糖凝胶模型中掺入了氯化钠，以呈现典型的头部射频负载，并且比水更受欢迎，以避免漩涡伪影。 分析仅使用来自中央切片(18)的时间序列数据来进行分析。使用以图像为中心的20x20 ROI来测量序列中每个时间帧的图像强度。均方根分数波动是在二次去趋势后计算的，漂移是从趋势线的极值获得的(图1，顶部)。计算了傅里叶谱(图1，中间)。‘Weisskoff分析’是使用大小从1x1到20x20的ROI进行的，如图1底部所示。在这种分析中，如果噪声是不相关的，则标准差(STD)应该减小为？(ROI中的体素数量)。来自射频源或梯度源的系统不稳定性往往会导致低空间频率的图像波动，这表现为体素之间的互相关。这进而导致STD偏离对1/(ROI宽度)的线性依赖，并随着ROI大小变得更大而触底，如图1(参见图1)所示。带虚线)。请注意，顶部绘图是使用最大的ROI大小执行的，有意选择来表示系统受限(而不是SNR受限)噪声。此外，通过由(时间序列平均图像)/(时间序列标准差图像)制成的地图中的ROI来测量信号对波动噪声比(SFNR)。最后，根据用噪声图像中的ROI测量的噪声计算SNR，该噪声图像是通过减去偶数时间帧的平均值和奇数时间帧的平均值并分割成平均信号而获得的。

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.