The image purification strategy constructs an intermediate distribution with aligned anatomical structures, which effectively corrects the spatial misalignment between real-world ultra-low-dose CT and normal-dose CT images and significantly enhances the structural preservation ability of denoising models. However, this strategy exhibits two inherent limitations. First, it suppresses noise only in the chest wall and bone regions while leaving the image background untreated. Second, it lacks a dedicated mechanism for denoising the lung parenchyma. To address these issues, we systematically redesign the original image purification strategy and propose an improved version termed IPv2. The proposed strategy introduces three core modules, namely Remove Background, Add noise, and Remove noise. These modules endow the model with denoising capability in both background and lung tissue regions during training data construction and provide a more reasonable evaluation protocol through refined label construction at the testing stage. Extensive experiments on our previously established real-world patient lung CT dataset acquired at 2% radiation dose demonstrate that IPv2 consistently improves background suppression and lung parenchyma restoration across multiple mainstream denoising models. The code is publicly available at https://github.com/MonkeyDadLufy/Image-Purification-Strategy-v2.

翻译：图像净化策略通过构建具有对齐解剖结构的中间分布，有效校正了真实世界超低剂量CT与正常剂量CT图像间的空间失准，显著提升了去噪模型的结构保持能力。然而，该策略存在两个固有局限：其一，它仅抑制胸壁与骨骼区域的噪声，而未对图像背景进行处理；其二，缺乏针对肺实质区域的专用去噪机制。为解决这些问题，我们系统性地重新设计了原始图像净化策略，提出了改进版本IPv2。该策略引入了三个核心模块——背景移除、噪声添加与噪声去除。这些模块在训练数据构建阶段赋予模型在背景与肺组织区域的双重去噪能力，并通过测试阶段优化的标签构建提供更合理的评估方案。在我们先前建立的2%辐射剂量采集的真实世界患者肺部CT数据集上进行的大量实验表明，IPv2在多种主流去噪模型中均能持续提升背景抑制与肺实质恢复性能。代码已公开于https://github.com/MonkeyDadLufy/Image-Purification-Strategy-v2。