DISGAN: Wavelet-informed Discriminator Guides GAN to MRI
  Super-resolution with Noise Cleaning
Qi Wang Lucas Mahler Julius Steiglechner Florian Birk Klaus Scheffler Gabriele Lohmann
MRI super-resolution (SR) and denoising tasks are fundamental challenges in
the field of deep learning, which have traditionally been treated as distinct
tasks with separate paired training data. In this paper, we propose an
innovative method that addresses both tasks simultaneously using a single deep
learning model, eliminating the need for explicitly paired noisy and clean
images during training. Our proposed model is primarily trained for SR, but
also exhibits remarkable noise-cleaning capabilities in the super-resolved
images. Instead of conventional approaches that introduce frequency-related
operations into the generative process, our novel approach involves the use of
a GAN model guided by a frequency-informed discriminator. To achieve this, we
harness the power of the 3D Discrete Wavelet Transform (DWT) operation as a
frequency constraint within the GAN framework for the SR task on magnetic
resonance imaging (MRI) data. Specifically, our contributions include: 1) a 3D
generator based on residual-in-residual connected blocks; 2) the integration of
the 3D DWT with $1\times 1$ convolution into a DWT+conv unit within a 3D Unet
for the discriminator; 3) the use of the trained model for high-quality image
SR, accompanied by an intrinsic denoising process. We dub the model "Denoising
Induced Super-resolution GAN (DISGAN)" due to its dual effects of SR image
generation and simultaneous denoising. Departing from the traditional approach
of training SR and denoising tasks as separate models, our proposed DISGAN is
trained only on the SR task, but also achieves exceptional performance in
denoising. The model is trained on 3D MRI data from dozens of subjects from the
Human Connectome Project (HCP) and further evaluated on previously unseen MRI
data from subjects with brain tumours and epilepsy to assess its denoising and
SR performance.