Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: k-Space Deep Learning for Accelerated MRI   The annihilating filter-based low-rank Hankel matrix approach (ALOHA) is one
of the state-of-the-art compressed sensing approaches that directly
interpolates the missing k-space data using low-rank Hankel matrix completion.
The success of ALOHA is due to the concise signal representation in the k-space
domain thanks to the duality between structured low-rankness in the k-space
domain and the image domain sparsity. Inspired by the recent mathematical
discovery that links convolutional neural networks to Hankel matrix
decomposition using data-driven framelet basis, here we propose a fully
data-driven deep learning algorithm for k-space interpolation. Our network can
be also easily applied to non-Cartesian k-space trajectories by simply adding
an additional regridding layer. Extensive numerical experiments show that the
proposed deep learning method consistently outperforms the existing
image-domain deep learning approaches.

33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con