Physics-Inspired Generative Models in Medical Imaging: A Review

TL;DR

This review discusses physics-inspired generative models like diffusion and Poisson flow models, highlighting their applications and future potential in medical imaging for improved accuracy and robustness.

Contribution

It provides a comprehensive overview of recent physics-inspired generative models and explores their applications and future research directions in medical imaging.

Findings

Physics-inspired GMs improve image reconstruction and analysis.

Models like DDPMs and PFGMs show high accuracy and robustness.

Future directions include unification and integration with VLMs.

Abstract

Physics-inspired Generative Models (GMs), in particular Diffusion Models (DMs) and Poisson Flow Models (PFMs), enhance Bayesian methods and promise great utility in medical imaging. This review examines the transformative role of such generative methods. First, a variety of physics-inspired GMs, including Denoising Diffusion Probabilistic Models (DDPMs), Score-based Diffusion Models (SDMs), and Poisson Flow Generative Models (PFGMs and PFGM++), are revisited, with an emphasis on their accuracy, robustness as well as acceleration. Then, major applications of physics-inspired GMs in medical imaging are presented, comprising image reconstruction, image generation, and image analysis. Finally, future research directions are brainstormed, including unification of physics-inspired GMs, integration with Vision-Language Models (VLMs), and potential novel applications of GMs. 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.