Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: K-space and Image Domain Collaborative Energy based Model for Parallel
  MRI Reconstruction   Decreasing magnetic resonance (MR) image acquisition times can potentially
make MR examinations more accessible. Prior arts including the deep learning
models have been devoted to solving the problem of long MRI imaging time.
Recently, deep generative models have exhibited great potentials in algorithm
robustness and usage flexibility. Nevertheless, no existing such schemes that
can be learned or employed directly to the k-space measurement. Furthermore,
how do the deep generative models work well in hybrid domain is also worth to
be investigated. In this work, by taking advantage of the deep en-ergy-based
models, we propose a k-space and image domain collaborative generative model to
comprehensively estimate the MR data from under-sampled measurement.
Experimental comparisons with the state-of-the-arts demonstrated that the
proposed hybrid method has less error in reconstruction and is more stable
under different acceleration factors.

9
The categories of this paper are: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-alg