发表机构
Oak Ridge National Laboratory（橡树岭国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究探究用跨多成像域训练的单一多模态扩散模型作为CT重建的通用先验，在三类不同CT数据集上验证其性能优于解析重建，为异构CT重建提供可复用基础先验。
AI 中文摘要
计算机断层扫描（CT）的通量受扫描时间限制，扫描时间随采集的投影数量及每个投影的探测器积分时间增加而增长。因此，从稀疏视图或低剂量测量中重建高质量体积数据依赖于信息先验，这类先验通常是针对特定扫描设置训练的神经网络，且在模态、几何或材料变化时需重新训练。本研究探究在多个成像域上训练的单一扩散模型是否可同时作为多种CT问题的先验。我们在三个模态、束流几何、材料及退化类型不同的数据集上评估所提方法，这些数据集涵盖锥束X射线CT成像的增材制造金属零件缺陷分析，以及平行束中子CT成像的混凝土微观结构，且均使用同一冻结模型。所提方法在所有三个案例中均优于解析重建方法，为面向异构CT重建问题的可复用基础先验迈出了一步。
英文摘要
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 projection. Reconstructing high-quality volumes from sparse-view or low-dose measurements therefore depends on using 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 reusable prior for heterogeneous CT reconstruction problems. We evaluate the proposed method using the same diffusion visual transformer model and normalized denoising strength on three datasets that differ in modality, beam geometry, material, and degradation type, spanning additively manufactured metal parts and concrete microstructure imaged with cone-bean X-ray CT and parallel-beam neutron CT respectively. The proposed method improves upon analytic reconstructions in all three cases, demonstrating transferability across the evaluated problems and providing a step toward a reusable foundation prior for heterogeneous CT reconstruction.
CommentsTo appear in SC26 Workshops proceedings
