A Domain Translation Framework with an Adversarial Denoising Diffusion
  Model to Generate Synthetic Datasets of Echocardiography Images
Cristiana Tiago Sten Roar Snare Jurica Sprem Kristin McLeod
Currently, medical image domain translation operations show a high demand
from researchers and clinicians. Amongst other capabilities, this task allows
the generation of new medical images with sufficiently high image quality,
making them clinically relevant. Deep Learning (DL) architectures, most
specifically deep generative models, are widely used to generate and translate
images from one domain to another. The proposed framework relies on an
adversarial Denoising Diffusion Model (DDM) to synthesize echocardiography
images and perform domain translation. Contrary to Generative Adversarial
Networks (GANs), DDMs are able to generate high quality image samples with a
large diversity. If a DDM is combined with a GAN, this ability to generate new
data is completed at an even faster sampling time. In this work we trained an
adversarial DDM combined with a GAN to learn the reverse denoising process,
relying on a guide image, making sure relevant anatomical structures of each
echocardiography image were kept and represented on the generated image
samples. For several domain translation operations, the results verified that
such generative model was able to synthesize high quality image samples: MSE:
11.50 +/- 3.69, PSNR (dB): 30.48 +/- 0.09, SSIM: 0.47 +/- 0.03. The proposed
method showed high generalization ability, introducing a framework to create
echocardiography images suitable to be used for clinical research purposes.