Abstract
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 from 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 retaining the benefits of explicit data consistency-based optimization. Our code is publicly available at this https URL.
Abstract (translated)
运动失真是磁共振成像(MRI)中普遍存在的问题,可能导致人口级成像研究中的误判或误描述。当前回顾性 rigid 内部切片运动纠正技术 jointly 优化的图像和运动参数估计。在本文中,我们使用深度网络来减少 joint 图像和运动参数搜索到只搜索Rigid 运动参数的搜索。我们的网络以两个输入:损坏的 k-空间数据以及运动参数作为函数产生重构。我们使用模拟的、运动损坏的 k-空间数据生成已知的运动参数。在测试时,我们估计未知的运动参数,通过最小化数据一致性损失在运动参数、基于网络的图像重构和获取的测量之间实现。模拟和现实的 2D 快速旋转 Echo 脑磁共振内部切片运动纠正实验实现了高重构精度,同时保留了明确的数据一致性优化的好处。我们的代码在此 https URL 上公开可用。
URL
https://arxiv.org/abs/2301.10365
