Information Technology University (ITU)-Punjab, Lahore, 54700, Pakistan.
Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co. Ltd., Seoul, 06524, South Korea.
Sci Rep. 2020 Mar 16;10(1):4786. doi: 10.1038/s41598-020-61705-9.
Multishot Magnetic Resonance Imaging (MRI) is a promising data acquisition technique that can produce a high-resolution image with relatively less data acquisition time than the standard spin echo. The downside of multishot MRI is that it is very sensitive to subject motion and even small levels of motion during the scan can produce artifacts in the final magnetic resonance (MR) image, which may result in a misdiagnosis. Numerous efforts have focused on addressing this issue; however, all of these proposals are limited in terms of how much motion they can correct and require excessive computational time. In this paper, we propose a novel generative adversarial network (GAN)-based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. First CG-SENSE reconstruction is employed to reconstruct an image from the motion-corrupted k-space data and then the GAN-based proposed framework is applied to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establish that the proposed method is robust and reduces several-fold the computational time reported by the current state-of-the-art technique.
多 shot 磁共振成像（MRI）是一种很有前途的数据采集技术，与标准的自旋回波相比，它可以用相对较少的数据采集时间生成高分辨率图像。多 shot MRI 的缺点是对受试者运动非常敏感，即使在扫描过程中出现很小程度的运动，也会在最终的磁共振（MR）图像中产生伪影，这可能导致误诊。许多研究都集中在解决这个问题上；然而，所有这些方案在能够纠正的运动程度方面都受到限制，并且需要过多的计算时间。在本文中，我们提出了一种基于生成对抗网络（GAN）的共轭梯度 SENSE（CG-SENSE）重建框架，用于多 shot MRI 中的运动校正。首先，CG-SENSE 重建用于从运动污染的 k 空间数据中重建图像，然后应用基于 GAN 的提出的框架来校正运动伪影。该方法在不同程度的运动、shot 数量和编码轨迹的合成污染数据上进行了严格评估。我们的分析（包括定量分析和定性/视觉分析）表明，该方法是稳健的，并将当前最先进技术报告的计算时间减少了数倍。
