用一个通用扩散模型解决多种CT重建问题，无需重新训练。
Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model
- 用跨领域的扩散模型作为通用先验，支持多模态CT重建。
- 在三种不同成像条件下均优于传统方法，重建质量显著提升。
- 适合需要快速适配新扫描条件的工业检测场景。
CT扫描速度受限于投影数量和探测器积分时间，因此从稀疏视角或低剂量数据中重建高质量体数据依赖于有效的先验信息。传统方法通常使用针对特定扫描设置训练的神经网络，一旦成像模态、几何结构或材料改变，需重新训练。本文探索是否可训练一个跨多个成像领域的单一扩散模型，作为多种CT问题的通用先验。我们在三个数据集上评估该方法：分别涉及锥束X射线CT对增材制造金属件缺陷分析，以及平行束中子断层成像对混凝土微观结构的观测，三者在模态、光束几何、材料及退化类型上均有差异。实验结果表明，使用同一冻结模型在所有场景下均优于经典重建方法，为异构CT重建提供了可复用的基础先验方案。
原文摘要 · Abstract (English)
Computed tomography (CT) throughput is limited by scan time, which grows with both the number of projections acquired and the detector integration time for each. Reconstructing high-quality volumes from sparse-view or low-dose measurements therefore depends on an informative prior, typically a neural network trained for one specific scan setting and retrained whenever the modality, geometry, or material changes. We investigate whether a single diffusion model trained across several imaging domains can instead serve as a prior for many CT problems simultaneously. We evaluate the proposed method using the same frozen model on three datasets that differ in modality, beam geometry, material, and degradation type, spanning flaw analysis in additively manufactured metal parts imaged with cone-beam X-ray CT and concrete microstructure imaged with parallel-beam neutron CT. Our proposed method out-performs analytic reconstructions in all three cases, providing a step toward a reusable foundation prior for heterogeneous CT reconstruction problems.
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