DM4CT：面向计算机断层扫描重建的扩散模型基准测试框架

摘要

扩散模型近期已成为解决逆问题的强大先验工具。尽管计算机断层扫描（CT）在理论上属于线性逆问题，但其实际应用面临诸多挑战，包括相关噪声、伪影结构、对系统几何构型的依赖以及数值范围失准等问题，这使得扩散模型在CT领域的直接应用比自然图像生成等领域更为困难。为系统评估扩散模型在此背景下的性能并与成熟重建方法进行对比，我们推出了DM4CT——一个专为CT重建设计的综合基准测试平台。DM4CT涵盖医学和工业领域的稀疏视角与含噪配置数据集。为深入探究扩散模型实际部署中的挑战，我们额外采集了高能同步辐射装置的高分辨率CT数据集，并在真实实验条件下评估所有方法。我们系统比较了十种最新扩散模型方法与七种强基线方法（包括模型驱动、无监督及有监督方法）。我们的分析为扩散模型在CT重建中的行为特征、优势与局限提供了细致洞察。真实世界数据集已公开于zenodo.org/records/15420527，代码库开源在github.com/DM4CT/DM4CT。

English

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-based, unsupervised, and supervised approaches. Our analysis provides detailed insights into the behavior, strengths, and limitations of diffusion models for CT reconstruction. The real-world dataset is publicly available at zenodo.org/records/15420527, and the codebase is open-sourced at github.com/DM4CT/DM4CT.

DM4CT：面向计算机断层扫描重建的扩散模型基准测试框架

DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction

摘要

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