Gao Qi, Chen Zhihao, Zeng Dong, Zhang Junping, Ma Jianhua, Shan Hongming
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China.
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China.
Med Image Anal. 2025 Oct;105:103710. doi: 10.1016/j.media.2025.103710. Epub 2025 Jul 8.
The generalization of deep learning-based low-dose computed tomography (CT) reconstruction models to doses unseen in the training data is important and remains challenging. Previous efforts heavily rely on paired data to improve the generalization performance and robustness through collecting either diverse CT data for re-training or a few test data for fine-tuning. Recently, diffusion models have shown promising and generalizable performance in low-dose CT (LDCT) reconstruction, however, they may produce unrealistic structures due to the CT image noise deviating from Gaussian distribution and imprecise prior information from the guidance of noisy LDCT images. In this paper, we propose a noise-inspired diffusion model for generalizable LDCT reconstruction, termed NEED, which tailors diffusion models for noise characteristics of each domain. First, we propose a novel shifted Poisson diffusion model to denoise projection data, which aligns the diffusion process with the noise model in pre-log LDCT projections. Second, we devise a doubly guided diffusion model to refine reconstructed images, which leverages LDCT images and initial reconstructions to more accurately locate prior information and enhance reconstruction fidelity. By cascading these two diffusion models for dual-domain reconstruction, our NEED requires only normal-dose data for training and can be effectively extended to various unseen dose levels during testing via a time step matching strategy. Extensive qualitative, quantitative, and segmentation-based evaluations on two datasets demonstrate that our NEED consistently outperforms state-of-the-art methods in reconstruction and generalization performance. Source code is made available at https://github.com/qgao21/NEED.
将基于深度学习的低剂量计算机断层扫描（CT）重建模型推广到训练数据中未见过的剂量是很重要的，但仍然具有挑战性。以前的努力严重依赖配对数据，通过收集多样化的CT数据进行重新训练或少量测试数据进行微调来提高泛化性能和鲁棒性。最近，扩散模型在低剂量CT（LDCT）重建中显示出有前景的和可推广的性能，然而，由于CT图像噪声偏离高斯分布以及来自有噪声的LDCT图像引导的先验信息不准确，它们可能会产生不现实的结构。在本文中，我们提出了一种用于可推广的LDCT重建的噪声启发扩散模型，称为NEED，它针对每个域的噪声特征定制扩散模型。首先，我们提出了一种新颖的移位泊松扩散模型来对投影数据进行去噪，该模型将扩散过程与预对数LDCT投影中的噪声模型对齐。其次，我们设计了一种双引导扩散模型来细化重建图像，该模型利用LDCT图像和初始重建来更准确地定位先验信息并提高重建保真度。通过级联这两个扩散模型进行双域重建，我们的NEED只需要正常剂量数据进行训练，并且在测试期间可以通过时间步匹配策略有效地扩展到各种未见过的剂量水平。在两个数据集上进行的广泛的定性、定量和基于分割的评估表明，我们的NEED在重建和泛化性能方面始终优于现有方法。源代码可在https://github.com/qgao21/NEED获取。
