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.

该研究开发了一种新的残差 - 残差稠密块发生器 (RRDG) 来解决基于 GAN 的三维 SISR，并提出了一种改进收敛行为的 Patch GAN 鉴别器来更好地建模脑图像纹理，并提出了一种新颖的解剖学保真度评估方法，这些开发通过一种简单，高效的方法结合起来，以实现影像和质地质量之间的平衡。