  Physics-inspired generative models, in particular diffusion and Poisson flow
models, enhance Bayesian methods and promise great utilities in medical
imaging. This review examines the transformative role of such generative
methods. First, a variety of physics-inspired generative models, including
Denoising Diffusion Probabilistic Models (DDPM), Score-based Diffusion Models,
and Poisson Flow Generative Models (PFGM and PFGM++), are revisited, with an
emphasis on their accuracy, robustness as well as acceleration. Then, major
applications of physics-inspired generative models in medical imaging are
presented, comprising image reconstruction, image generation, and image
analysis. Finally, future research directions are brainstormed, including
unification of physics-inspired generative models, integration with
vision-language models (VLMs),and potential novel applications of generative
models. Since the development of generative methods has been rapid, this review
will hopefully give peers and learners a timely snapshot of this new family of
physics-driven generative models and help capitalize their enormous potential
for medical imaging.
