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.

翻译：物理启发生成模型，特别是扩散模型与泊松流模型，增强了贝叶斯方法，并在医学影像中展现出巨大的应用潜力。本文综述了此类生成方法的变革性作用。首先，回顾了多种物理启发生成模型，包括去噪扩散概率模型（DDPM）、基于分数的扩散模型以及泊松流生成模型（PFGM 和 PFGM++），重点探讨了其准确性、鲁棒性以及加速技术。随后，介绍了物理启发生成模型在医学影像中的主要应用，涵盖图像重建、图像生成与图像分析。最后，展望了未来的研究方向，包括物理启发生成模型的统一、与视觉-语言模型（VLMs）的融合，以及生成模型的潜在新颖应用。鉴于生成方法发展迅速，本综述旨在为同行与学习者及时呈现这一新兴物理驱动生成模型家族的概貌，并助力发掘其在医学影像领域的巨大潜力。