标题： 基于模型的去噪扩散模型的直接双能CT材料分解 显示英文标题

标题： Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model

摘要： 双能X射线计算机断层扫描（DECT）是一种先进技术，它能够利用X射线线性衰减与能量的依赖关系，在无需手动分割的情况下自动分解临床图像中的材料。 然而，大多数方法在图像域中进行材料分解，作为重建后的后处理步骤，但此过程未考虑束硬化效应，导致结果不理想。 在本工作中，我们提出了一种称为基于双能分解模型的扩散（DEcomp-MoD）的深度学习方法，用于定量材料分解，该方法直接将DECT投影数据转换为材料图像。 该算法基于将光谱DECT模型的知识纳入深度学习训练损失，并在材料图像域中结合基于分数的去噪扩散先验。 重要的是，推理优化损失直接以sinogram作为输入，并通过基于模型的条件扩散模型转换为材料图像，从而保证结果的一致性。 我们通过定量和定性估计评估了所提出的DEcomp-MoD方法在低剂量AAPM数据集合成DECT sinogram上的性能。 最后，我们展示了DEcomp-MoD优于最先进的无监督分数模型和监督深度学习网络，具有部署用于临床诊断的潜力。 显示英文摘要

摘要： Dual-energy X-ray Computed Tomography (DECT) constitutes an advanced technology which enables automatic decomposition of materials in clinical images without manual segmentation using the dependency of the X-ray linear attenuation with energy. However, most methods perform material decomposition in the image domain as a post-processing step after reconstruction but this procedure does not account for the beam-hardening effect and it results in sub-optimal results. In this work, we propose a deep learning procedure called Dual-Energy Decomposition Model-based Diffusion (DEcomp-MoD) for quantitative material decomposition which directly converts the DECT projection data into material images. The algorithm is based on incorporating the knowledge of the spectral DECT model into the deep learning training loss and combining a score-based denoising diffusion learned prior in the material image domain. Importantly the inference optimization loss takes as inputs directly the sinogram and converts to material images through a model-based conditional diffusion model which guarantees consistency of the results. We evaluate the performance with both quantitative and qualitative estimation of the proposed DEcomp-MoD method on synthetic DECT sinograms from the low-dose AAPM dataset. Finally, we show that DEcomp-MoD outperform state-of-the-art unsupervised score-based model and supervised deep learning networks, with the potential to be deployed for clinical diagnosis.