Penn Statistics in Imaging and Visualization Endeavor, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
J Neuroimaging. 2023 Nov-Dec;33(6):941-952. doi: 10.1111/jon.13147. Epub 2023 Aug 16.
Multicenter study designs involving a variety of MRI scanners have become increasingly common. However, these present the issue of biases in image-based measures due to scanner or site differences. To assess these biases, we imaged 11 volunteers with multiple sclerosis (MS) with scan and rescan data at four sites.
Images were acquired on Siemens or Philips scanners at 3 Tesla. Automated white matter lesion detection and whole-brain, gray and white matter, and thalamic volumetry were performed, as well as expert manual delineations of T1 magnetization-prepared rapid acquisition gradient echo and T2 fluid-attenuated inversion recovery lesions. Random-effect and permutation-based nonparametric modeling was performed to assess differences in estimated volumes within and across sites.
Random-effect modeling demonstrated model assumption violations for most comparisons of interest. Nonparametric modeling indicated that site explained >50% of the variation for most estimated volumes. This expanded to >75% when data from both Siemens and Philips scanners were included. Permutation tests revealed significant differences between average inter- and intrasite differences in most estimated brain volumes (P < .05). The automatic activation of spine coil elements during some acquisitions resulted in a shading artifact in these images. Permutation tests revealed significant differences between thalamic volume measurements from acquisitions with and without this artifact.
Differences in brain volumetry persisted across MR scanners despite protocol harmonization. These differences were not well explained by variance component modeling; however, statistical innovations for mitigating intersite differences show promise in reducing biases in multicenter studies of MS.
涉及多种 MRI 扫描仪的多中心研究设计变得越来越普遍。然而,由于扫描仪或地点的差异,这些研究存在基于图像的测量值存在偏差的问题。为了评估这些偏差,我们对 11 名多发性硬化症(MS)志愿者进行了成像,在四个地点进行了扫描和重扫数据。
在 3T 上使用西门子或飞利浦扫描仪采集图像。进行了自动白质病变检测以及全脑、灰质和白质以及丘脑容积测量,以及 T1 磁化准备快速获取梯度回波和 T2 液体衰减反转恢复病变的专家手动描绘。进行随机效应和基于置换的非参数建模,以评估各站点内和站点间估计体积的差异。
随机效应模型显示,大多数感兴趣的比较都违反了模型假设。非参数建模表明,站点解释了大多数估计体积的>50%的变化。当包括西门子和飞利浦扫描仪的数据时,这一比例扩展到了>75%。置换检验显示,大多数估计脑容量的站点间和站点内差异的平均值之间存在显著差异(P<.05)。在某些采集过程中,脊柱线圈元件的自动激活导致这些图像出现阴影伪影。置换检验显示,有和没有这种伪影的采集的丘脑体积测量值之间存在显著差异。
尽管进行了方案协调,但脑容积测量值在磁共振扫描仪之间仍存在差异。这些差异不能很好地用方差分量建模来解释;然而,用于减轻站点间差异的统计创新在减少多发性硬化症多中心研究中的偏差方面显示出了希望。