AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany.
Neuroimage. 2023 Jul 15;275:120176. doi: 10.1016/j.neuroimage.2023.120176. Epub 2023 May 18.
Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.
头部运动会降低磁共振图像质量，并已被证明会影响神经形态分析。因此，头部运动的量化不仅具有神经科学意义，也具有临床应用价值，例如，在大脑形态的统计分析中控制运动，或者作为神经学研究中的一个感兴趣变量。然而，无标记光学头部跟踪的准确性在很大程度上尚未得到探索。此外，迄今为止，还没有针对一般的、大多数健康人群队列的头部运动的定量分析。在这项工作中，我们提出了一种稳健的深度相机数据配准方法，该方法可以敏感地估计顺从参与者的微小头部运动。我们的方法在三个验证实验中优于供应商提供的方法：1. 与 fMRI 运动轨迹的相似性作为低频参考，2. 独立采集的呼吸信号的恢复作为高频参考，3. 与结构 T1 加权 MRI 中的基于图像的质量指标的相关性。除了核心算法外，我们还建立了一个分析管道，该管道可以计算每个时间间隔或每个序列的平均运动得分，以便纳入下游分析。我们将该管道应用于 Rhineland 研究，这是一项大型人群队列研究，在该研究中，我们复制了年龄和体重指数（BMI）作为运动相关因素，并表明头部运动会随着扫描会话的持续时间而显著增加。我们观察到这种会话内增加与年龄、BMI 和性别之间的弱但显著的相互作用。fMRI 和基于相机的运动得分之间的高相关性进一步表明，在没有更好的措施来控制统计分析中的运动的情况下，fMRI 运动估计可以用作替代得分。
