DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
Diffusion models have recently emerged as powerful priors for solving inverse problems. While computed tomography (CT) is theoretically a linear inverse problem, it poses many practical challenges. These include correlated noise, artifact structures, reliance on system geometry, and misaligned value ranges, which make the direct application of diffusion models more difficult than in domains like natural image generation. To systematically evaluate how diffusion models perform in this context and compare them with established reconstruction methods, we introduce DM4CT, a comprehensive benchmark for CT reconstruction. DM4CT includes datasets from both medical and industrial domains with spa...
| Authors | Jiayang Shi, Daniel M. Pelt, K. Joost Batenburg |
|---|---|
| Primary category | eess.IV |
| Published | 20 February 2026 |
| Last updated at source | 20 February 2026 |
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