Gunter Jeffrey L, Bernstein Matt A, Borowski Brett J, Ward Chadwick P, Britson Paula J, Felmlee Joel P, Schuff Norbert, Weiner Michael, Jack Clifford R
Mayo Clinic and Foundation, Rochester, Minnesota 55902, USA.
Med Phys. 2009 Jun;36(6):2193-205. doi: 10.1118/1.3116776.
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.
描述多站点、多供应商定量研究在实际实施过程中面临的挑战；描述阿尔茨海默病神经成像计划（ADNI）研究中使用的MRI体模及分析软件，阐明该系统在测量扫描仪性能、评估梯度场非线性校正能力方面的效用，以及在多站点研究中恢复无几何缩放误差的人脑图像的能力。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预分布系统研究的结果，以及在3年时间里，该体模在58个参与ADNI研究的站点的现场使用经验的结果。单个体模几何测量的固有变异系数估计在10的4次方分之3 - 5的范围内。体模测量能够准确检测图像中的线性和非线性缩放。通过体模非线性测量可以很容易地评估梯度去扭曲方法。基于体模的缩放校正可将人类图像中观察到的几何漂移减少三分之一或更多。然而，扫描之间体模的修复或更换是一个混杂因素。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，以减少人类图像中的系统误差。减少测量误差应能降低测量偏差，并提高AD治疗试验中脑结构变化率测量的统计功效。在多站点研究中纳入ADNI体模测量的最大实际价值可能在于通过中央监测识别扫描仪误差。这种方法已导致识别出系统误差，包括站点对自身梯度硬件的错误识别、自动匀场功能的禁用以及激光对准光校准错误。如果未被检测到，这些误差将导致超过25%的参与ADNI研究的站点的定量指标不准确。
