Brain MRI super-resolution using 3D generative adversarial networks
This work addresses super-resolution for 3D medical imaging, which could aid in better diagnosis and analysis, but it is incremental as it adapts existing SRGAN methods to 3D convolutions.
The authors tackled the problem of generating high-resolution MRI scans from low-resolution images using a 3D generative adversarial network, achieving promising results that improve classical interpolation methods.
In this work we propose an adversarial learning approach to generate high resolution MRI scans from low resolution images. The architecture, based on the SRGAN model, adopts 3D convolutions to exploit volumetric information. For the discriminator, the adversarial loss uses least squares in order to stabilize the training. For the generator, the loss function is a combination of a least squares adversarial loss and a content term based on mean square error and image gradients in order to improve the quality of the generated images. We explore different solutions for the upsampling phase. We present promising results that improve classical interpolation, showing the potential of the approach for 3D medical imaging super-resolution. Source code available at https://github.com/imatge-upc/3D-GAN-superresolution