Reproduce “3D Diffusion Models for Medical Imaging” on OpenResearch

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About this paper

Frank-Peter Schilling, Daniel Barco, Philipp Denzel, Marc Stadelmann

The authors review diffusion and flow-based methods for generating three-dimensional medical images, focusing on how compressed latent representations make high-resolution volumes more tractable. They explain why slice-wise generation can disrupt anatomical continuity and why full-volume modeling remains computationally demanding. In a reported CT comparison, MAISI-v2 has FID 2.32 versus HA-GAN’s 13.60; these results are protocol-specific, and clinical reliability remains unresolved.

Summary by alphaXiv.

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