Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders
  (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)
Fernando Vega Abdoljalil Addeh M. Ethan MacDonald
In this work, a denoising Cycle-GAN (Cycle Consistent Generative Adversarial
Network) is implemented to yield high-field, high resolution, high
signal-to-noise ratio (SNR) Magnetic Resonance Imaging (MRI) images from
simulated low-field, low resolution, low SNR MRI images. Resampling and
additive Rician noise were used to simulate low-field MRI. Images were utilized
to train a Denoising Autoencoder (DAE) and a Cycle-GAN, with paired and
unpaired cases. Both networks were evaluated using SSIM and PSNR image quality
metrics. This work demonstrates the use of a generative deep learning model
that can outperform classical DAEs to improve low-field MRI images and does not
require image pairs.