Measurement of MRI scanner performance with the ADNI phantom
Measurement of MRI scanner performance with the ADNI phantom
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DOI：
10.1118/1.3116776
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发表时间：
2009-06-01
期刊：
影响因子：
3.8
通讯作者：
Jack, Clifford R.
中科院分区：
文献类型：
--
作者：
Gunter, Jeffrey L.;Bernstein, Matt A.;Jack, Clifford R.
本研究的目的如下：描述多中心、多供应商定量研究的实际实施挑战;描述阿尔茨海默病神经成像倡议（ADNI）研究中使用的MRI体模和分析软件，说明系统用于测量扫描仪性能的实用性，以及评估梯度场非线性校正的能力：以及在多地点研究中恢复人脑图像而没有几何缩放误差。ADNI是一项大型多中心研究，每个中心都有自己的体模副本。体模和分析软件的设计是根据分销前系统研究的结果和在58个入组ADNI研究中心3年内使用体模的现场经验的结果而提出的。单个体模中几何结构测量固有的估计变异系数在3-5/10（4）的范围内。体模测量准确地检测图像中的线性和非线性缩放。梯度展开方法很容易通过体模非线性测量进行评估。基于比例缩放的校正将人类图像中观察到的几何漂移减少了三分之一或更多。然而，在扫描之间修复或更换体模是一个混杂因素。ADNI体模可用于评估扫描仪性能和后处理图像校正的有效性，以减少人体图像中的系统误差。减少测量误差应减少测量偏倚，并增加AD治疗试验中脑结构变化率测量的统计功效。在多中心研究中结合ADNI体模测量的最大实用价值可能是通过中央监测识别扫描仪错误。这种方法导致系统错误的识别，包括站点错误识别其自身的梯度硬件和禁用自动填隙，以及错误校准的激光对准灯。如果未被发现，这些错误将导致超过25%的ADNI研究中心的定量指标不精确。
The objectives of this study are as follows: to describe practical implementation challenges of multisite, multivendor quantitative studies; to describe the MRI phantom and analysis software used in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, illustrate the utility of the system for measuring scanner performance, the ability to assess gradient field nonlinearity corrections: and to recover human brain images without geometric scaling errors in multisite studies. ADNI is a large multicenter study with each center having its own copy of the phantom. The design of the phantom and analysis software are presented as results from predistribution systematics studies and results from field experience with the phantom at 58 enrolling ADNI sites over a 3 year period. The estimated coefficients of variation intrinsic to measurements of geometry in a single phantom are in the range of 3-5 parts in 10(4). Phantom measurements accurately detect linear and nonlinear scaling in images. Gradient unwarping methods are readily assessed by phantom nonlinearity measurements. Phantom-based scaling correction reduces observed geometric drift in human images by one-third or more. Repair or replacement of phantoms between scans, however, is a confounding factor. The ADNI phantom can be used to assess both scanner performance and the validity of postprocessing image corrections in order to reduce systematic errors in human images. Reduced measurement errors should decrease measurement bias and increase statistical power for measurements of rates of change in the brain structure in AD treatment trials. Perhaps the greatest practical value of incorporating ADNI phantom measurements in a multisite study is to identify scanner errors through central monitoring. This approach has resulted in identification of system errors including sites misidentification of their own gradient hardware and the disabling of autoshim, and a miscalibrated laser alignment light. If undetected, these errors would have contributed to imprecision in quantitative metrics at over 25% of all enrolling ADNI sites.