In dynamic Magnetic Resonance Imaging (MRI), k-space is typically undersampled due to limited scan time, resulting in aliasing artifacts in the image domain. Hence, dynamic MR reconstruction requires not only modeling spatial frequency components in the x and y directions of k-space but also considering temporal redundancy. Most previous works rely on image-domain regularizers (priors) to conduct MR reconstruction. In contrast, we focus on interpolating the undersampled k-space before obtaining images with Fourier transform. In this work, we connect masked image modeling with k-space interpolation and propose a novel Transformer-based k-space Global Interpolation Network, termed k-GIN. Our k-GIN learns global dependencies among low- and high-frequency components of 2D+t k-space and uses it to interpolate unsampled data. Further, we propose a novel k-space Iterative Refinement Module (k-IRM) to enhance the high-frequency components learning. We evaluate our approach on 92 in-house 2D+t cardiac MR subjects and compare it to MR reconstruction methods with image-domain regularizers. Experiments show that our proposed k-space interpolation method quantitatively and qualitatively outperforms baseline methods. Importantly, the proposed approach achieves substantially higher robustness and generalizability in cases of highly-undersampled MR data. For video presentation, poster, GIF results and code please check our project page: https://jzpeterpan.github.io/k-gin.github.io/.

翻译：在动态磁共振成像中，由于扫描时间有限，k空间通常采用欠采样策略，导致图像域出现混叠伪影。因此，动态MR重建不仅需要建模k空间x和y方向的空间频率分量，还需考虑时间维度的冗余性。现有方法大多依赖图像域正则化先验进行MR重建，而我们提出在通过傅里叶变换获得图像前，直接对欠采样k空间进行插值。本研究将掩码图像建模与k空间插值相结合，提出了一种基于Transformer的新型k空间全局插值网络（k-GIN）。k-GIN可学习二维时间序列k空间中低高频分量的全局依赖关系，并利用该依赖关系对未采样数据进行插值。此外，我们进一步提出新型k空间迭代细化模块（k-IRM）以增强高频分量学习。基于92例内部二维时间序列心脏MR数据的评估表明，相比图像域正则化MR重建方法，所提出的k空间插值方法在定性和定量指标上均优于基线方法。尤其在高欠采样MR数据场景下，该方法表现出显著更高的鲁棒性和泛化能力。视频演示、海报、GIF结果及代码详见项目主页：https://jzpeterpan.github.io/k-gin.github.io/。