MRI with multiple protocols is commonly used for diagnosis, but it suffers
from a long acquisition time, which yields the image quality vulnerable to say
motion artifacts. To accelerate, various methods have been proposed to
reconstruct full images from under-sampled k-space data. However, these
algorithms are inadequate for two main reasons. Firstly, aliasing artifacts
generated in the image domain are structural and non-local, so that sole image
domain restoration is insufficient. Secondly, though MRI comprises multiple
protocols during one exam, almost all previous studies only employ the
reconstruction of an individual protocol using a highly distorted undersampled
image as input, leaving the use of fully-sampled short protocol (say T1) as
complementary information highly underexplored. In this work, we address the
above two limitations by proposing a Dual Domain Recurrent Network (DuDoRNet)
with deep T1 prior embedded to simultaneously recover k-space and images for
accelerating the acquisition of MRI with a long imaging protocol. Specifically,
a Dilated Residual Dense Network (DRDNet) is customized for dual domain
restorations from undersampled MRI data. Extensive experiments on different
sampling patterns and acceleration rates demonstrate that our method
consistently outperforms state-of-the-art methods, and can reconstruct
high-quality MRI.

该研究提出了一种基于 DDS 网络和 DRDNet 的 MRI 加速方法，可以快速重建从欠采样 MRI 数据中获得的 k 空间数据，得到高质量的 MRI 图像。