Low-dose computed tomography (LDCT) denoising tasks face significant challenges in practical imaging scenarios. Supervised methods encounter difficulties in real-world scenarios as there are no paired data for training. Moreover, when applied to datasets with varying noise patterns, these methods may experience decreased performance owing to the domain gap. Conversely, unsupervised methods do not require paired data and can be directly trained on real-world data. However, they often exhibit inferior performance compared to supervised methods. To address this issue, it is necessary to leverage the strengths of these supervised and unsupervised methods. In this paper, we propose a novel domain adaptive noise reduction framework (DANRF), which integrates both knowledge transfer and style generalization learning to effectively tackle the domain gap problem. Specifically, an iterative knowledge transfer method with knowledge distillation is selected to train the target model using unlabeled target data and a pre-trained source model trained with paired simulation data. Meanwhile, we introduce the mean teacher mechanism to update the source model, enabling it to adapt to the target domain. Furthermore, an iterative style generalization learning process is also designed to enrich the style diversity of the training dataset. We evaluate the performance of our approach through experiments conducted on multi-source datasets. The results demonstrate the feasibility and effectiveness of our proposed DANRF model in multi-source LDCT image processing tasks. Given its hybrid nature, which combines the advantages of supervised and unsupervised learning, and its ability to bridge domain gaps, our approach is well-suited for improving practical low-dose CT imaging in clinical settings. Code for our proposed approach is publicly available at .

中文翻译：

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通过迭代知识迁移和风格泛化学习进行领域自适应降噪


低剂量计算机断层扫描（LDCT）去噪任务在实际成像场景中面临着重大挑战。由于没有用于训练的配对数据，监督方法在现实场景中遇到困难。此外，当应用于具有不同噪声模式的数据集时，这些方法可能会由于域间隙而导致性能下降。相反，无监督方法不需要配对数据，可以直接在现实世界数据上进行训练。然而，与监督方法相比，它们通常表现出较差的性能。为了解决这个问题，有必要利用这些有监督和无监督方法的优势。在本文中，我们提出了一种新颖的领域自适应降噪框架（DANRF），它将知识迁移和风格泛化学习结合起来，以有效解决领域差距问题。具体来说，选择具有知识蒸馏的迭代知识转移方法来使用未标记的目标数据和使用配对模拟数据训练的预训练源模型来训练目标模型。同时，我们引入平均教师机制来更新源模型，使其能够适应目标域。此外，还设计了迭代风格泛化学习过程来丰富训练数据集的风格多样性。我们通过在多源数据集上进行的实验来评估我们方法的性能。结果证明了我们提出的 DANRF 模型在多源 LDCT 图像处理任务中的可行性和有效性。鉴于其混合性质，结合了监督学习和无监督学习的优点，以及弥合领域差距的能力，我们的方法非常适合改善临床环境中的实用低剂量 CT 成像。 我们提议的方法的代码可在 上公开获取。