Super-Resolution using GANs for Medical Imaging

Abstract

Generative Adversarial Models (GANs) have been quite popular and are currently and active area of research. They can be used for generative new data and study adversarial samples and attacks. We have used the similar approach to apply super-resolution to medical images. In Radiology MRI is a commonly used method to produce medical imaging but the limitations of lab equipment and health hazard of being in an MRI radiation environment to obtain good quality scans lead to lower quality scans and also it takes a lot of time to get a high-resolution data. This problem can be solved by using super-resolution using deep learning as a post-processing step to improve the resolution of the scans. Super-resolution is a process of generating higher resolution images from lower resolution data. For this, we are proposing a generative adversarial network architecture which is a dual neural network designed to generate lifelike images. In this deep learning algorithm, two neural networks compete with each other to improve alternatively. Given a training set, this technique learns to generate new data with the same statistics as the training set. To apply this technique to our problem statement we are using generator as the network to improve the resolution and discriminator as a network to train generator better. We used transfer learning in our generative neural network and training our discriminator from scratch and using the perceptual loss [1] to train our network. This will help in improving the performance of the network. We are using Lung MRI scans of tuberculosis with a set of 216 MRI samples containing around 60-130 channels each and each channel having 512x512 dimensions.