Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simultaneous use of Individual and Joint Regularization Terms in
  Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI
  Acquisitions   Multi-contrast images are commonly acquired together to maximize
complementary diagnostic information, albeit at the expense of longer scan
times. A time-efficient strategy to acquire high-quality multi-contrast images
is to accelerate individual sequences and then reconstruct undersampled data
with joint regularization terms that leverage common information across
contrasts. However, these terms can cause features that are unique to a subset
of contrasts to leak into the other contrasts. Such leakage-of-features may
appear as artificial tissues, thereby misleading diagnosis. The goal of this
study is to develop a compressive sensing method for multi-channel
multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared
information while preventing feature leakage. Joint regularization terms group
sparsity and colour total variation are used to exploit common features across
images while individual sparsity and total variation are also used to prevent
leakage of distinct features across contrasts. The multi-channel multi-contrast
reconstruction problem is solved via a fast algorithm based on Alternating
Direction Method of Multipliers. The proposed method is compared against using
only individual and only joint regularization terms in reconstruction.
Comparisons were performed on single-channel simulated and multi-channel
in-vivo datasets in terms of reconstruction quality and neuroradiologist reader
scores. The proposed method demonstrates rapid convergence and improved image
quality for both simulated and in-vivo datasets. Furthermore, while
reconstructions that solely use joint regularization terms are prone to
leakage-of-features, the proposed method reliably avoids leakage via
simultaneous use of joint and individual terms, thereby holding great promise
for clinical use.

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The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an