Head motion is an omnipresent confounder of magnetic resonance image (MRI) analyses as it systematically affects morphometric measurements, even when visual quality control is performed. In order to estimate subtle head motion, that remains undetected by experts, we introduce a deep learning method to predict in-scanner head motion directly from T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) images using motion estimates from an in-scanner depth camera as ground truth. Since we work with data from compliant healthy participants of the Rhineland Study, head motion and resulting imaging artifacts are less prevalent than in most clinical cohorts and more difficult to detect. Our method demonstrates improved performance compared to state-of-the-art motion estimation methods and can quantify drift and respiration movement independently. Finally, on unseen data, our predictions preserve the known, significant correlation with age.

翻译：头部运动是磁共振图像（MRI）分析中普遍存在的混杂因素，它会系统性地影响形态测量结果，即使经过视觉质量控制也是如此。为了估计专家未能发现的细微头部运动，我们引入了一种深度学习方法，该方法利用深度摄像机获得的运动估计作为真值，直接从T1加权（T1w）、T2加权（T2w）和液体衰减反转恢复（FLAIR）图像预测扫描仪内头部运动。由于我们使用来自莱茵兰研究的健康合规参与者数据，与大多数临床队列相比，头部运动及其产生的成像伪影不那么普遍且更难检测。我们的方法相比最先进的运动估计方法表现出改进的性能，并且可以独立量化漂移和呼吸运动。最后，在未见数据上，我们的预测保留了与年龄已知的显著相关性。