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In this work, a denoising Cycle-GAN (Cycle Consistent Generative AdversarialNetwork) is implemented to yield high-field, high resolution, highsignal-to-noise ratio (SNR) Magnetic Resonance Imaging (MRI) images fromsimulated low-field, low resolution, low SNR MRI images. Resampling andadditive Rician noise were used to simulate low-field MRI. Images were utilizedto train a Denoising Autoencoder (DAE) and a Cycle-GAN, with paired andunpaired cases. Both networks were evaluated using SSIM and PSNR image qualitymetrics. This work demonstrates the use of a generative deep learning modelthat can outperform classical DAEs to improve low-field MRI images and does notrequire image pairs.

Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)