Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D High-Quality Magnetic Resonance Image Restoration in Clinics Using
  Deep Learning   Shortening acquisition time and reducing the motion artifacts are two of the
most essential concerns in magnetic resonance imaging. As a promising solution,
deep learning-based high-quality MR image restoration has been investigated to
generate higher resolution and motion artifact-free MR images from lower
resolution images acquired with shortened acquisition time, without costing
additional acquisition time or modifying the pulse sequences. However, numerous
problems still exist to prevent deep learning approaches from becoming
practical in the clinic environment. Specifically, most of the prior works
focus solely on the network model but ignore the impact of various downsampling
strategies on the acquisition time. Besides, the long inference time and high
GPU consumption are also the bottlenecks to deploy most of the prior works in
clinics. Furthermore, prior studies employ random movement in retrospective
motion artifact generation, resulting in uncontrollable severity of motion
artifact. More importantly, doctors are unsure whether the generated MR images
are trustworthy, making diagnosis difficult. To overcome all these problems, we
employed a unified 2D deep learning neural network for both 3D MRI super
resolution and motion artifact reduction, demonstrating such a framework can
achieve better performance in 3D MRI restoration tasks compared to other states
of the art methods and remains the GPU consumption and inference time
significantly low, thus easier to deploy. We also analyzed several downsampling
strategies based on the acceleration factor, including multiple combinations of
in-plane and through-plane downsampling, and developed a controllable and
quantifiable motion artifact generation method. At last, the pixel-wise
uncertainty was calculated and used to estimate the accuracy of the generated
image, providing additional information for reliable diagnosis.

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The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg