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[2602.18589] DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
Summary
The paper presents DM4CT, a benchmark for evaluating diffusion models in computed tomography (CT) reconstruction, addressing practical challenges and comparing performance against established methods.
Why It Matters
This research is significant as it systematically evaluates the effectiveness of diffusion models in CT reconstruction, a critical area in medical imaging. By providing a comprehensive benchmark and real-world datasets, it enhances understanding of model performance and limitations, potentially improving diagnostic imaging techniques.
Key Takeaways
- DM4CT introduces a benchmark for assessing diffusion models in CT reconstruction.
- The study highlights challenges in applying diffusion models to CT, such as noise and artifact structures.
- Ten diffusion-based methods were benchmarked against seven established reconstruction techniques.
- Real-world datasets were utilized to evaluate model performance under practical conditions.
- The findings provide insights into the strengths and limitations of diffusion models in medical imaging.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2602.18589 (eess) [Submitted on 20 Feb 2026] Title:DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction Authors:Jiayang Shi, Daniel M. Pelt, K. Joost Batenburg View a PDF of the paper titled DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction, by Jiayang Shi and 2 other authors View PDF HTML (experimental) Abstract: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 sparse-view and noisy configurations. To explore the challenges of deploying diffusion models in practice, we additionally acquire a high-resolution CT dataset at a high-energy synchrotron facility and evaluate all methods under real experimental conditions. We benchmark ten recent diffusion-based methods alongside seven strong baselines, including model-base...
