Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Regularized Spherical Polar Fourier Diffusion MRI with Optimal
  Dictionary Learning   Compressed Sensing (CS) takes advantage of signal sparsity or compressibility
and allows superb signal reconstruction from relatively few measurements. Based
on CS theory, a suitable dictionary for sparse representation of the signal is
required. In diffusion MRI (dMRI), CS methods were proposed to reconstruct
diffusion-weighted signal and the Ensemble Average Propagator (EAP), and there
are two kinds of Dictionary Learning (DL) methods: 1) Discrete Representation
DL (DR-DL), and 2) Continuous Representation DL (CR-DL). DR-DL is susceptible
to numerical inaccuracy owing to interpolation and regridding errors in a
discretized q-space. In this paper, we propose a novel CR-DL approach, called
Dictionary Learning - Spherical Polar Fourier Imaging (DL-SPFI) for effective
compressed-sensing reconstruction of the q-space diffusion-weighted signal and
the EAP. In DL-SPFI, an dictionary that sparsifies the signal is learned from
the space of continuous Gaussian diffusion signals. The learned dictionary is
then adaptively applied to different voxels using a weighted LASSO framework
for robust signal reconstruction. The adaptive dictionary is proved to be
optimal. Compared with the start-of-the-art CR-DL and DR-DL methods proposed by
Merlet et al. and Bilgic et al., espectively, our work offers the following
advantages. First, the learned dictionary is proved to be optimal for Gaussian
diffusion signals. Second, to our knowledge, this is the first work to learn a
voxel-adaptive dictionary. The importance of the adaptive dictionary in EAP
reconstruction will be demonstrated theoretically and empirically. Third,
optimization in DL-SPFI is only performed in a small subspace resided by the
SPF coefficients, as opposed to the q-space approach utilized by Merlet et al.
The experiment results demonstrate the advantages of DL-SPFI over the original
SPF basis and Bilgic et al.'s method.

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The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: math
12
The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: stat
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The categories of this paper are: cmp-lg