发表机构
Yanshan University; Capital Medical University; Beijing Friendship Hospital; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Agency for Science, Technology and Research (A*STAR)（燕山大学; 首都医科大学; 北京友谊医院; 中国科学院深圳先进技术研究院; 新加坡科技研究局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对超低剂量CT去噪中器官特异性方法泛化差的问题，提出基于深度字典网络的可解释基础模型，通过级联稀疏编码与动态字典实现统一多器官去噪，性能超越现有方法。
AI 中文摘要
超低剂量计算机断层扫描（ULDCT）可减少辐射暴露，但会遭受严重噪声，从而降低诊断图像质量。现有的基于深度学习的去噪方法通常以器官特异性的方式进行训练，导致在异质性多器官成像场景中的泛化能力有限。基础模型为统一的多器官去噪提供了一种有前景的“一体式”范式。然而，其架构可解释性差，且依赖启发式训练策略。为解决这些局限性，我们提出了一种基于深度字典网络（DDN）的架构可解释基础模型，用于统一的多器官ULDCT去噪。受多层稀疏表示理论启发，DDN将卷积稀疏编码层与迭代软阈值级联，提供了固有的架构可解释性。此外，每一层内嵌入了动态字典模块和阈值生成模块，以增强表示能力。我们通过在超过一百万张多器官正常剂量CT图像上，从高斯噪声输入中恢复干净图像来进行DDN预训练。稀疏正则化被额外施加于潜在特征表示上，引导网络学习紧凑且对噪声鲁棒的先验。完整架构在多器官ULDCT数据集上联合微调，使单个统一模型能够在不同解剖区域执行去噪。大量实验验证了我们的方法达到了最先进的性能，并在所有多器官ULDCT方法中持续超越竞争对手。
英文摘要
Ultra-low-dose computed tomography (ULDCT) reduces radiation exposure but suffers from severe noise that degrades diagnostic image quality. Existing deep learning-based denoising methods are typically trained in an organ-specific fashion, resulting in limited generalization across heterogeneous multi?organ imaging scenarios. Foundation models present a promising all-in-one paradigm for unified multi-organ denoising. However, their architectures suffer from poor interpretability and rely on heuristic training strategies. To address these limitations, we propose an architecture?interpretable foundation model based on the deep dictionary network (DDN) for unified multi-organ ULDCT denoising. Inspired by multilayer sparse representation theory, DDN cascades convolutional sparse coding layers with iterative soft-thresholding, providing inherent architectural interpretability. Furthermore, a dynamic dictionary module and a threshold generation module are embedded within each layer to enhance representation ability. We conduct DDN pre-training on more than one million multi-organ normal-dose CT images by recovering clean images from Gaussian-noised inputs. Sparse regularization is additionally imposed on latent feature representations, guiding the network to learn compact and noise-robust priors. The complete architecture is jointly fine-tuned on multi-organ ULDCT datasets, enabling a single unified model to perform denoising across diverse anatomical regions. Extensive experiments validate that our proposed method achieves state-of-the?art performance and consistently surpasses competing ULDCT methods across all mul
