Ultra-low dose CT (uLDCT) significantly reduces radiation exposure but introduces severe noise and artifacts. It also leads to substantial spatial misalignment between uLDCT and normal dose CT (NDCT) image pairs. This poses challenges for directly applying existing denoising networks trained on synthetic noise or aligned data. To address this core challenge in uLDCT denoising, this paper proposes an innovative denoising framework based on an Image Purification (IP) strategy. First, we construct a real clinical uLDCT lung dataset. Then, we propose an Image Purification strategy that generates structurally aligned uLDCT-NDCT image pairs, providing a high-quality data foundation for network training. Building upon this, we propose a Frequency-domain Flow Matching (FFM) model, which works synergistically with the IP strategy to excellently preserve the anatomical structure integrity of denoised images. Experiments on the real clinical dataset demonstrate that our IP strategy significantly enhances the performance of multiple mainstream denoising models on the uLDCT task. Notably, our proposed FFM model combined with the IP strategy achieves state-of-the-art (SOTA) results in anatomical structure preservation. This study provides an effective solution to the data mismatch problem in real-world uLDCT denoising. Code and dataset are available at https://github.com/MonkeyDadLufy/flow-matching.

翻译：超低剂量CT（uLDCT）能显著降低辐射暴露，但会引入严重的噪声和伪影。它还导致uLDCT与常规剂量CT（NDCT）图像对之间存在显著的空间错位。这对直接应用基于合成噪声或对齐数据训练的现有去噪网络构成了挑战。为应对uLDCT去噪中的这一核心挑战，本文提出了一种基于图像净化（IP）策略的创新去噪框架。首先，我们构建了一个真实的临床uLDCT肺部数据集。接着，我们提出了一种图像净化策略，用于生成结构对齐的uLDCT-NDCT图像对，为网络训练提供高质量的数据基础。在此基础上，我们提出了一个频域流匹配（FFM）模型，该模型与IP策略协同工作，能出色地保持去噪后图像的解剖结构完整性。在真实临床数据集上的实验表明，我们的IP策略显著提升了多种主流去噪模型在uLDCT任务上的性能。值得注意的是，我们提出的FFM模型结合IP策略在解剖结构保持方面达到了最先进的（SOTA）效果。本研究为真实世界uLDCT去噪中的数据失配问题提供了一个有效的解决方案。代码和数据集可在 https://github.com/MonkeyDadLufy/flow-matching 获取。