  Low-field (LF) MRI scanners have the power to revolutionize medical imaging
by providing a portable and cheaper alternative to high-field MRI scanners.
However, such scanners are usually significantly noisier and lower quality than
their high-field counterparts. The aim of this paper is to improve the SNR and
overall image quality of low-field MRI scans to improve diagnostic capability.
To address this issue, we propose a Nested U-Net neural network architecture
super-resolution algorithm that outperforms previously suggested deep learning
methods with an average PSNR of 78.83 and SSIM of 0.9551. We tested our network
on artificial noisy downsampled synthetic data from a major T1 weighted MRI
image dataset called the T1-mix dataset. One board-certified radiologist scored
25 images on the Likert scale (1-5) assessing overall image quality, anatomical
structure, and diagnostic confidence across our architecture and other
published works (SR DenseNet, Generator Block, SRCNN, etc.). We also introduce
a new type of loss function called natural log mean squared error (NLMSE). In
conclusion, we present a more accurate deep learning method for single image
super-resolution applied to synthetic low-field MRI via a Nested U-Net
architecture.
