  This paper proposes to learn analysis transform network for dynamic magnetic
resonance imaging (LANTERN) with small dataset. 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
using adaptively trained CNN. (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 reconstruction accuracy is better when the amount of data is
limited. Our model is able to fully exploit the redundancy in spatial and
temporal of dynamic MR images. We performed quantitative and qualitative
analysis of cardiac datasets at different acceleration factors (2x-11x) and
different undersampling modes. In comparison with state-of-the-art methods,
extensive experiments show that our method achieves consistent better
reconstruction performance on the MRI reconstruction in terms of three
quantitative metrics (PSNR, SSIM and HFEN) under different undersamling
patterns and acceleration factors.
