Dynamic magnetic resonance imaging (MRI) plays an indispensable role in
cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled
but the image reconstruction poses a great challenge of high-dimensional
processing. This challenge leads to necessitate extensive training data in many
deep learning reconstruction methods. This work proposes a novel and efficient
approach, leveraging a dimension-reduced separable learning scheme that excels
even with highly limited training data. We further integrate it with
spatiotemporal priors to develop a Deep Separable Spatiotemporal Learning
network (DeepSSL), which unrolls an iteration process of a reconstruction model
with both temporal low-rankness and spatial sparsity. Intermediate outputs are
visualized to provide insights into the network's behavior and enhance its
interpretability. Extensive results on cardiac cine datasets show that the
proposed DeepSSL is superior to the state-of-the-art methods visually and
quantitatively, while reducing the demand for training cases by up to 75%. And
its preliminary adaptability to cardiac patients has been verified through
experienced radiologists' and cardiologists' blind reader study. Additionally,
DeepSSL also benefits for achieving the downstream task of cardiac segmentation
with higher accuracy and shows robustness in prospective real-time cardiac MRI.

这篇论文提出了一种新颖高效的方法，结合深度学习和时空先验知识，可以显著降低高维数据处理的挑战，从而实现快速成像，并在心脏诊断中超过了现有方法，同时减少对训练数据的需求，达到高准确度的心脏分割。

深度可分离时空学习用于快速动态心脏 MRI

Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

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。

DuDoRNet: 基于深层 T1 先验知识的双域循环神经网络用于快速 MRI 重建

DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI  Reconstruction with Deep T1 Prior

This paper presents a deep learning method for faster magnetic resonance
imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and
provides a rationale for why the proposed approach works well. Uniform
subsampling is used in the time-consuming phase-encoding direction to capture
high-resolution image information, while permitting the image-folding problem
dictated by the Poisson summation formula. To deal with the localization
uncertainty due to image folding, very few low-frequency k-space data are
added. Training the deep learning net involves input and output images that are
pairs of Fourier transforms of the subsampled and fully sampled k-space data.
Numerous experiments show the remarkable performance of the proposed method;
only 29% of k-space data can generate images of high quality as effectively as
standard MRI reconstruction with fully sampled data.

本文利用深度学习方法，通过对 k 空间数据进行次 - Nyquist 采样策略降维，来提高磁共振成像的速度，并提供了理由为什么该方法表现良好。通过在时间消耗方向上采用均匀子采样捕捉高分辨率图像信息，同时允许由 Poisson 求和公式指导的图像折叠问题。为了处理由图像折叠产生的定位不确定性，只添加了极少量的低频 k - 空间数据。大量的实验证明了该方法的显着性能表现，只需要用到 29% 的 k - 空间数据就可以像使用完全采样数据的标准 MRI 重建一样高效生成高质量的图像。