论文标题
一种新的方法，用于纠正MRI扫描中多个离散的刚性内部运动伪像
A Novel Approach for Correcting Multiple Discrete Rigid In-Plane Motions Artefacts in MRI Scans
论文作者
论文摘要
在MRI扫描过程中，患者运动在实践中经常发生运动伪像，经常在临床上无法使用并需要重新扫描扫描。尽管已经采用了许多方法来改善患者运动的影响，但实际上这些方法通常不足。在本文中，我们提出了一种新的方法，该方法使用具有两个输入分支的深神经网络去除运动伪像，使用运动的时间来区分患者的姿势。第一个分支收到一个在单个患者姿势期间收集的$ K $空间数据的子集，第二个分支收到了收集的$ K $ - 空间数据的其余部分。该方法可以应用于由患者多种运动产生的人工制品。此外，它可用于纠正$ k $ - 空间采样不足的情况，以缩短扫描时间，当使用诸如并行成像或压缩感测的方法时，通常是常见的。对模拟和实际MRI数据的实验结果表明我们方法的功效。
Motion artefacts created by patient motion during an MRI scan occur frequently in practice, often rendering the scans clinically unusable and requiring a re-scan. While many methods have been employed to ameliorate the effects of patient motion, these often fall short in practice. In this paper we propose a novel method for removing motion artefacts using a deep neural network with two input branches that discriminates between patient poses using the motion's timing. The first branch receives a subset of the $k$-space data collected during a single patient pose, and the second branch receives the remaining part of the collected $k$-space data. The proposed method can be applied to artefacts generated by multiple movements of the patient. Furthermore, it can be used to correct motion for the case where $k$-space has been under-sampled, to shorten the scan time, as is common when using methods such as parallel imaging or compressed sensing. Experimental results on both simulated and real MRI data show the efficacy of our approach.
