  We propose a novel architecture called MLP-SRGAN, which is a single-dimension
Super Resolution Generative Adversarial Network (SRGAN) that utilizes
Multi-Layer Perceptron Mixers (MLP-Mixers) along with convolutional layers to
upsample in the slice direction. MLP-SRGAN is trained and validated using high
resolution (HR) FLAIR MRI from the MSSEG2 challenge dataset. The method was
applied to three multicentre FLAIR datasets (CAIN, ADNI, CCNA) of images with
low spatial resolution in the slice dimension to examine performance on
held-out (unseen) clinical data. Upsampled results are compared to several
state-of-the-art SR networks. For images with high resolution (HR) ground
truths, peak-signal-to-noise-ratio (PSNR) and structural similarity index
(SSIM) are used to measure upsampling performance. Several new structural,
no-reference image quality metrics were proposed to quantify sharpness (edge
strength), noise (entropy), and blurriness (low frequency information) in the
absence of ground truths. Results show MLP-SRGAN results in sharper edges, less
blurring, preserves more texture and fine-anatomical detail, with fewer
parameters, faster training/evaluation time, and smaller model size than
existing methods. Code for MLP-SRGAN training and inference, data generators,
models and no-reference image quality metrics will be available at
https://github.com/IAMLAB-Ryerson/MLP-SRGAN.
