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.

翻译：在动态磁共振成像中，由于扫描时间有限，通常会对k空间进行欠采样，导致图像域出现混叠伪影。因此，动态磁共振重建不仅需要建模k空间x方向和y方向的空间频率分量，还需考虑时间冗余性。以往大多数研究依赖图像域正则化先验进行磁共振重建。与此不同，我们聚焦于在通过傅里叶变换获取图像之前，对欠采样的k空间进行插值。本研究将掩膜图像建模与k空间插值相结合，提出一种基于Transformer的新型k空间全局插值网络（k-GIN）。该网络能够学习二维加时间k空间中低频与高频分量的全局依赖关系，并据此对未采样数据进行插值。此外，我们还提出一种新颖的k空间迭代精化模块（k-IRM），以增强高频分量的学习能力。我们使用92例内部采集的二维加时间心脏磁共振数据评估该方法，并与采用图像域正则化器的磁共振重建方法进行比较。实验表明，我们提出的k空间插值方法在定量和定性指标上均优于基线方法。更重要的是，该方法在高度欠采样的磁共振数据场景中展现出显著更高的鲁棒性和泛化能力。