Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders
(DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)
- URL: http://arxiv.org/abs/2307.06338v1
- Date: Wed, 12 Jul 2023 00:01:00 GMT
- Title: Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders
(DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)
- Authors: Fernando Vega, Abdoljalil Addeh, M. Ethan MacDonald
- Abstract summary: Cycle Consistent Generative Adversarial Network (GAN) is implemented to yield high-field, high resolution, high signal-to-noise ratio (SNR) Magnetic Resonance Imaging (MRI) images.
Images were utilized to train a Denoising Autoencoder (DAE) and a Cycle-GAN, with paired and unpaired cases.
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.
- Score: 68.8204255655161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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