跨分布扩散先验驱动的稀疏视图CT迭代重建
Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT
AI总结：
本文提出CDPIR框架，通过整合跨分布扩散先验与模型驱动迭代重建方法，解决稀疏视图CT在分布外场景下的重建问题，提升图像质量与泛化能力。
摘要：
稀疏视图CT（SVCT）重建通过提高时间分辨率和降低辐射剂量，但其临床应用受限于视图减少和域偏移导致的伪影，特别是在分布外（OOD）场景中性能下降。本文提出跨分布扩散先验驱动的迭代重建框架（CDPIR），通过训练扩展的扩散变压器（DiT）架构的SiT骨干网络，建立统一的随机插值框架，并利用分类器自由引导（CFG）跨多个数据集进行训练。通过在训练中随机丢弃条件信息，模型学习了领域特定和不变的先验，增强了泛化能力。在采样过程中，基于全局敏感性的变压器扩散模型在统一的随机插值框架内利用跨分布先验，实现灵活稳定的多分布到噪声插值路径和解耦采样策略，从而提高对分布外重建的适应性。通过交替数据保真度和采样更新，我们的模型在SVCT重建中实现了最先进的性能，具有优异的细节保持能力。大量实验表明，CDPIR显著优于现有方法，特别是在分布外条件下，突显了其鲁棒性和在挑战性成像场景中的临床价值。
英文摘要：
Sparse-View CT (SVCT) reconstruction enhances temporal resolution and reduces radiation dose, yet its clinical use is hindered by artifacts due to view reduction and domain shifts from scanner, protocol, or anatomical variations, leading to performance degradation in out-of-distribution (OOD) scenarios. In this work, we propose a Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction (CDPIR) framework to tackle the OOD problem in SVCT. CDPIR integrates cross-distribution diffusion priors, derived from a Scalable Interpolant Transformer (SiT), with model-based iterative reconstruction methods. Specifically, we train a SiT backbone, an extension of the Diffusion Transformer (DiT) architecture, to establish a unified stochastic interpolant framework, leveraging Classifier-Free Guidance (CFG) across multiple datasets. By randomly dropping the conditioning with a null embedding during training, the model learns both domain-specific and domain-invariant priors, enhancing generalizability. During sampling, the globally sensitive transformer-based diffusion model exploits the cross-distribution prior within the unified stochastic interpolant framework, enabling flexible and stable control over multi-distribution-to-noise interpolation paths and decoupled sampling strategies, thereby improving adaptation to OOD reconstruction. By alternating between data fidelity and sampling updates, our model achieves state-of-the-art performance with superior detail preservation in SVCT reconstructions. Extensive experiments demonstrate that CDPIR significantly outperforms existing approaches, particularly under OOD conditions, highlighting its robustness and potential clinical value in challenging imaging scenarios.
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