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)分析的无处不在的混乱者,因为它系统地影响测光测量,即使进行了视觉质量控制。为了估计专家仍未察觉到的微妙头部运动,我们引入了一种深层次的学习方法,直接从T1加权(T1w)、T2加权(T2w)和液态减速反转恢复(FLAIR)图像中预测扫描深度摄像头的运动估计数作为地面真理。由于我们使用莱茵兰研究、头部运动和由此产生的成像文物符合要求的健康参与者提供的数据,在大多数临床组群中并不那么普遍,而且更难探测。我们的方法显示与最新运动估计方法相比性能的改善,可以独立地量化漂移和呼吸运动。最后,根据不可见的数据,我们的预测保留了已知的、重要的与年龄的相关性。</s>