Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion correction techniques jointly optimize estimates of the image and the motion parameters. In this paper, we use a deep network to reduce the joint image-motion parameter search to a search over rigid motion parameters alone. Our network produces a reconstruction as a function of two inputs: corrupted k-space data and motion parameters. We train the network using simulated, motion-corrupted k-space data generated with known motion parameters. At test-time, we estimate unknown motion parameters by minimizing a data consistency loss between the motion parameters, the network-based image reconstruction given those parameters, and the acquired measurements. Intra-slice motion correction experiments on simulated and realistic 2D fast spin echo brain MRI achieve high reconstruction fidelity while providing the benefits of explicit data consistency optimization. Our code is publicly available at https://www.github.com/nalinimsingh/neuroMoCo.

翻译：运动伪影是MRI中普遍存在的问题，导致群体水平的影像研究出现误诊或特征误判。当前的回顾性刚性层内运动校正技术通过联合优化图像和运动参数的估计值来解决问题。本文利用深度网络将图像-运动参数的联合搜索缩减为仅对刚性运动参数的搜索。我们的网络生成的重建结果取决于两个输入：受扰动的k空间数据和运动参数。我们使用已知运动参数生成的模拟受运动扰动的k空间数据来训练网络。在测试阶段，通过最小化运动参数、基于这些参数的网络图像重建结果与采集测量值之间的数据一致性损失来估计未知的运动参数。在模拟和真实二维快速自旋回波脑MRI上的层内运动校正实验实现了高重建保真度，同时具有显式数据一致性优化的优势。我们的代码已公开在 https://www.github.com/nalinimsingh/neuroMoCo。