Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enhanced generative adversarial network for 3D brain MRI
  super-resolution   Single image super-resolution (SISR) reconstruction for magnetic resonance
imaging (MRI) has generated significant interest because of its potential to
not only speed up imaging but to improve quantitative processing and analysis
of available image data. Generative Adversarial Networks (GAN) have proven to
perform well in recovering image texture detail, and many variants have
therefore been proposed for SISR. In this work, we develop an enhancement to
tackle GAN-based 3D SISR by introducing a new residual-in-residual dense block
(RRDG) generator that is both memory efficient and achieves state-of-the-art
performance in terms of PSNR (Peak Signal to Noise Ratio), SSIM (Structural
Similarity) and NRMSE (Normalized Root Mean Squared Error) metrics. We also
introduce a patch GAN discriminator with improved convergence behavior to
better model brain image texture. We proposed a novel the anatomical fidelity
evaluation of the results using a pre-trained brain parcellation network.
Finally, these developments are combined through a simple and efficient method
to balance etween image and texture quality in the final output.

29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph