  Image-to-image translation is a vital component in medical imaging
processing, with many uses in a wide range of imaging modalities and clinical
scenarios. Previous methods include Generative Adversarial Networks (GANs) and
Diffusion Models (DMs), which offer realism but suffer from instability and
lack uncertainty estimation. Even though both GAN and DM methods have
individually exhibited their capability in medical image translation tasks, the
potential of combining a GAN and DM to further improve translation performance
and to enable uncertainty estimation remains largely unexplored. In this work,
we address these challenges by proposing a Cascade Multi-path Shortcut
Diffusion Model (CMDM) for high-quality medical image translation and
uncertainty estimation. To reduce the required number of iterations and ensure
robust performance, our method first obtains a conditional GAN-generated prior
image that will be used for the efficient reverse translation with a DM in the
subsequent step. Additionally, a multi-path shortcut diffusion strategy is
employed to refine translation results and estimate uncertainty. A cascaded
pipeline further enhances translation quality, incorporating residual averaging
between cascades. We collected three different medical image datasets with two
sub-tasks for each dataset to test the generalizability of our approach. Our
experimental results found that CMDM can produce high-quality translations
comparable to state-of-the-art methods while providing reasonable uncertainty
estimations that correlate well with the translation error.
