Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation
  scans using Linked Denoising Diffusion Probabilistic Models
Rowan Bradbury Katherine A. Vallis Bartlomiej W. Papiez
The rapid advancement of Artificial Intelligence (AI) in biomedical imaging
and radiotherapy is hindered by the limited availability of large imaging data
repositories. With recent research and improvements in denoising diffusion
probabilistic models (DDPM), high quality synthetic medical scans are now
possible. Despite this, there is currently no way of generating multiple
related images, such as a corresponding ground truth which can be used to train
models, so synthetic scans are often manually annotated before use. This
research introduces a novel architecture that is able to generate multiple,
related PET-CT-tumour mask pairs using paired networks and conditional
encoders. Our approach includes innovative, time step-controlled mechanisms and
a `noise-seeding' strategy to improve DDPM sampling consistency. While our
model requires a modified perceptual loss function to ensure accurate feature
alignment we show generation of clearly aligned synthetic images and
improvement in segmentation accuracy with generated images.