MRI, a widespread non-invasive medical imaging modality, is highly sensitive to patient motion. Despite many attempts over the years, motion correction remains a difficult problem and there is no general method applicable to all situations. We propose a retrospective method for motion quantification and correction to tackle the problem of in-plane rigid-body motion, apt for classical 2D Spin-Echo scans of the brain, which are regularly used in clinical practice. Due to the sequential acquisition of k-space, motion artifacts are well localized. The method leverages the power of deep neural networks to estimate motion parameters in k-space and uses a model-based approach to restore degraded images to avoid ''hallucinations''. Notable advantages are its ability to estimate motion occurring in high spatial frequencies without the need of a motion-free reference. The proposed method operates on the whole k-space dynamic range and is moderately affected by the lower SNR of higher harmonics. As a proof of concept, we provide models trained using supervised learning on 600k motion simulations based on motion-free scans of 43 different subjects. Generalization performance was tested with simulations as well as in-vivo. Qualitative and quantitative evaluations are presented for motion parameter estimations and image reconstruction. Experimental results show that our approach is able to obtain good generalization performance on simulated data and in-vivo acquisitions.

翻译：磁共振成像（MRI）作为一种广泛使用的无创医学影像技术，对患者运动高度敏感。尽管历经多年探索，运动校正仍是一个难题，且缺乏适用于所有场景的通用方法。本文针对临床常规使用的经典二维自旋回波脑部扫描，提出一种适用于平面刚体运动的回顾性运动量化与校正方法。由于k空间采用顺序采集方式，运动伪影具有良好的局域性。该方法利用深度神经网络在k空间中估计运动参数，并采用模型驱动方法修复退化图像以避免“幻觉”产生。其显著优势在于无需无运动参考图像即可估计高频空间运动。所提方法适用于全k空间动态范围，且对高次谐波的低信噪比具有适度鲁棒性。作为概念验证，我们基于43名受试者的无运动扫描数据，通过监督学习训练了60万次运动仿真模型。通过仿真与活体实验验证泛化性能，并从运动参数估计与图像重建两方面进行定性与定量评估。实验结果表明，该方法在仿真数据与活体采集数据中均展现出良好的泛化性能。