剂量感知的冷扩散与物理一致性用于可泛化的低剂量CT重建
Dose-Aware Cold Diffusion with Physics Consistency for Generalizable Low-Dose CT Reconstruction
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中文总结 AI 辅助
针对低剂量CT重建在连续剂量水平上泛化差的问题，提出剂量感知冷扩散框架DACD，结合物理一致性校正，在三个基准上超越现有方法，实现稳健泛化。
中文摘要 AI 辅助
在计算机断层扫描中降低辐射剂量会显著降低图像质量，并对准确且临床可靠的重建构成挑战。尽管近期方法在低剂量CT中展现出潜力，但它们往往难以在连续且未见过的剂量水平上泛化，导致伪影和解剖细节的丢失。为解决这些局限，我们提出了剂量感知的冷扩散（DACD），一种物理一致的重建框架，在冷扩散过程中将辐射剂量显式建模为连续潜在因子。所提出的DACD框架整合了基于图像的剂量感知、多尺度结构先验提取和剂量校准的步骤分配，以自适应地引导去噪轨迹。此外，在反向细化过程中加入了迭代的正向-反向投影校正，以强制执行投影域的数据一致性。在三个公共基准（包括Mayo-2020、Mayo-2016和LoDoPaB-CT）上的大量实验表明，DACD在定量准确性和视觉保真度方面持续优于最先进的基于扩散和物理引导的方法，特别是在超低剂量条件下。结果表明，DACD在连续剂量范围（包括训练中未见过的剂量水平）内实现了稳健的泛化。
英文摘要
Reducing radiation dose in computed tomography significantly degrades image quality and poses challenges for accurate and clinically reliable reconstruction. While recent approaches have shown promise for low-dose CT, they often struggle to generalize across continuous and previously unseen dose levels, leading to artifacts and loss of anatomical detail. To address these limitations, we propose Dose-Aware Cold Diffusion (DACD), a physics-consistent reconstruction framework that explicitly models radiation dose as a continuous latent factor within a cold diffusion process. The proposed DACD framework integrates image-based dose-aware perception, multi-scale structural prior extraction, and dose-calibrated step allocation to adaptively guide the denoising trajectory. In addition, an iterative forward-backprojection correction is incorporated into the reverse refinement process to enforce projection-domain data consistency. Extensive experiments on three public benchmarks, including Mayo-2020, Mayo-2016, and LoDoPaB-CT, demonstrate that DACD consistently outperforms state-of-the-art diffusion-based and physics-guided methods in both quantitative accuracy and visual fidelity, particularly under ultra-low-dose conditions. The results show that DACD achieves robust generalization across a continuous range of dose levels, including those unseen during training.
