Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model Learning: Primal Dual Networks for Fast MR imaging   Magnetic resonance imaging (MRI) is known to be a slow imaging modality and
undersampling in k-space has been used to increase the imaging speed. However,
image reconstruction from undersampled k-space data is an ill-posed inverse
problem. Iterative algorithms based on compressed sensing have been used to
address the issue. In this work, we unroll the iterations of the primal-dual
hybrid gradient algorithm to a learnable deep network architecture, and
gradually relax the constraints to reconstruct MR images from highly
undersampled k-space data. The proposed method combines the theoretical
convergence guarantee of optimi-zation methods with the powerful learning
capability of deep networks. As the constraints are gradually relaxed, the
reconstruction model is finally learned from the training data by updating in
k-space and image domain alternatively. Experi-ments on in vivo MR data
demonstrate that the proposed method achieves supe-rior MR reconstructions from
highly undersampled k-space data over other state-of-the-art image
reconstruction methods.

18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg