Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An untrained deep learning method for reconstructing dynamic magnetic
  resonance images from accelerated model-based data   The purpose of this work is to implement physics-based regularization as a
stopping condition in tuning an untrained deep neural network for
reconstructing MR images from accelerated data. The ConvDecoder neural network
was trained with a physics-based regularization term incorporating the spoiled
gradient echo equation that describes variable-flip angle (VFA) data.
Fully-sampled VFA k-space data were retrospectively accelerated by factors of
R={8,12,18,36} and reconstructed with ConvDecoder (CD), ConvDecoder with the
proposed regularization (CD+r), locally low-rank (LR) reconstruction, and
compressed sensing with L1-wavelet regularization (L1). Final images from CD+r
training were evaluated at the \emph{argmin} of the regularization loss;
whereas the CD, LR, and L1 reconstructions were chosen optimally based on
ground truth data. The performance measures used were the normalized root-mean
square error, the concordance correlation coefficient (CCC), and the structural
similarity index (SSIM). The CD+r reconstructions, chosen using the stopping
condition, yielded SSIMs that were similar to the CD (p=0.47) and LR SSIMs
(p=0.95) across R and that were significantly higher than the L1 SSIMs
(p=0.04). The CCC values for the CD+r T1 maps across all R and subjects were
greater than those corresponding to the L1 (p=0.15) and LR (p=0.13) T1 maps,
respectively. For R > 12 (<4.2 minutes scan time), L1 and LR T1 maps exhibit a
loss of spatially refined details compared to CD+r. We conclude that the use of
an untrained neural network together with a physics-based regularization loss
shows promise as a measure for determining the optimal stopping point in
training without relying on fully-sampled ground truth data.

12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph