Current models based on deep learning for low-dose CT denoising rely heavily on paired data and generalize poorly. Even the more concerned diffusion models need to learn the distribution of clean data for reconstruction, which is difficult to satisfy in medical clinical applications. At the same time, self-supervised-based methods face the challenge of significant degradation of generalizability of models pre-trained for the current dose to expand to other doses. To address these issues, this work proposes a novel method of TUnable-geneRalizatioN Diffusion (TurnDiff) powered by self-supervised contextual sub-data for low-dose CT reconstruction. Firstly, a contextual subdata self-enhancing similarity strategy is designed for denoising centered on the LDCT projection domain, which provides an initial prior for the subsequent progress. Subsequently, the initial prior is used to combine knowledge distillation with a deep combination of latent diffusion models for optimizing image details. The pre-trained model is used for inference reconstruction, and the pixel-level self-correcting fusion technique is proposed for fine-grained reconstruction of the image domain to enhance the image fidelity, using the initial prior and the LDCT image as a guide. In addition, the technique is flexibly applied to the generalization of upper and lower doses or even unseen doses. Dual-domain strategy cascade for self-supervised LDCT denoising, TurnDiff requires only LDCT projection domain data for training and testing. Comprehensive evaluation on both benchmark datasets and real-world data demonstrates that TurnDiff consistently outperforms state-of-the-art methods in both reconstruction and generalization.

翻译：当前基于深度学习的低剂量CT去噪模型严重依赖配对数据且泛化能力较差。即使是更受关注的扩散模型也需要学习干净数据的分布以进行重建，这在医学临床应用中难以满足。同时，基于自监督的方法面临预训练模型在当前剂量下泛化能力显著下降的挑战，难以扩展到其他剂量。为解决这些问题，本研究提出了一种新颖的基于自监督上下文子数据的可调泛化扩散方法（TurnDiff）用于低剂量CT重建。首先，针对以LDCT投影域为中心的去噪任务，设计了一种上下文子数据自增强相似性策略，为后续进程提供初始先验。随后，利用该初始先验将知识蒸馏与潜在扩散模型深度融合以优化图像细节。预训练模型用于推理重建，并提出像素级自校正融合技术，以初始先验和LDCT图像为指导，对图像域进行细粒度重建以增强图像保真度。此外，该技术可灵活应用于上下剂量甚至未见剂量的泛化任务。通过自监督LDCT去噪的双域级联策略，TurnDiff仅需LDCT投影域数据进行训练和测试。在基准数据集和真实数据上的综合评估表明，TurnDiff在重建性能和泛化能力上均持续优于现有先进方法。