This paper proposes to learn analysis transform network for dynamic magnetic resonance imaging (LANTERN). Integrating the strength of CS-MRI and deep learning, the proposed framework is highlighted in three components: (ⅰ) The spatial and temporal domains are sparsely constrained by adaptively trained convolutional filters; (ⅱ) We introduce an end-to-end framework to learn the parameters in LANTERN to solve the difficulty of parameter selection in traditional methods; (ⅲ) Compared to existing deep learning reconstruction methods, our experimental results show that our paper has encouraging capability in exploiting the spatial and temporal redundancy of dynamic MR images. We performed quantitative and qualitative analysis of cardiac reconstructions at different acceleration factors ($ 2 \times $-$ 11 \times $) with different undersampling patterns. In comparison with two state-of-the-art methods, experimental results show that our method achieved encouraging performances.

中文翻译：

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灯笼：学习用于动态磁共振成像的分析变换网络

本文提出学习动态磁共振成像（LANTERN）的分析变换网络。结合CS-MRI和深度学习的优势，提出的框架在以下三个部分中得到了强调：（ⅰ）时空领域受到自适应训练的卷积滤波器的稀疏约束；（ⅱ）我们引入了一个端到端的框架来学习LANTERN中的参数，以解决传统方法中参数选择的困难；（ⅲ）与现有的深度学习重建方法相比，我们的实验结果表明，本文具有利用动态MR图像的时空冗余的令人鼓舞的能力。我们在不同的欠采样模式下，以不同的加速因子（$ 2 \乘以$-$ 11 \乘以$）对心脏重建进行了定量和定性分析。