Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image Reconstruction for MRI using Deep CNN Priors Trained without
  Groundtruth   We propose a new plug-and-play priors (PnP) based MR image reconstruction
method that systematically enforces data consistency while also exploiting
deep-learning priors. Our prior is specified through a convolutional neural
network (CNN) trained without any artifact-free ground truth to remove
undersampling artifacts from MR images. The results on reconstructing
free-breathing MRI data into ten respiratory phases show that the method can
form high-quality 4D images from severely undersampled measurements
corresponding to acquisitions of about 1 and 2 minutes in length. The results
also highlight the competitive performance of the method compared to several
popular alternatives, including the TGV regularization and traditional UNet3D.

8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg