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Physics-Inspired Generative Models in Medical Imaging.

Dennis Hein^1,2, Afshin Bozorgpour³, Dorit Merhof³

¹Department of Physics, KTH Royal Institute of Technology, Stockholm, Sweden.

Annual Review of Biomedical Engineering

|May 1, 2025

Summary

Physics-inspired generative models, including diffusion and Poisson flow models, are revolutionizing medical imaging. This review highlights their applications in reconstruction, generation, and analysis, paving the way for future advancements.

Keywords:

Bayesian theorem PFGM++Poisson flow generative model consistency model diffusion model image analysis image reconstruction image/data synthesis medical imaging physics-inspired generative models

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Physics

Background:

Generative models (GMs) are increasingly vital in medical imaging.
Physics-inspired GMs, such as diffusion and Poisson flow models, offer enhanced Bayesian methods.
These models show significant promise for improving various medical imaging tasks.

Purpose of the Study:

To review the role of physics-inspired generative models in medical imaging.
To examine the accuracy, robustness, and acceleration of these models.
To outline future research directions for generative methods in this field.

Main Methods:

Review of denoising diffusion probabilistic models, score-based diffusion models, and Poisson flow generative models (PFGM++).
Analysis of model performance focusing on accuracy, robustness, and acceleration.
Exploration of applications in medical image reconstruction, generation, and analysis.

Main Results:

Physics-inspired GMs demonstrate significant utility across medical imaging applications.
Key models like diffusion and Poisson flow models show promise for enhanced performance.
The review identifies current capabilities and limitations of these advanced GMs.

Conclusions:

Physics-inspired GMs are transformative for medical imaging, enhancing Bayesian methods.
Future research should focus on unifying these models, integrating them with vision-language models, and exploring novel applications.
This review provides a timely overview to leverage the potential of physics-driven GMs in medical imaging.