Low-dose computed tomography (CT) plays a significant role in reducing the radiation risk in clinical applications. However, lowering the radiation dose will significantly degrade the image quality. With the rapid development and wide application of deep learning, it has brought new directions for the development of low-dose CT imaging algorithms. Therefore, we propose a fully unsupervised one sample diffusion model (OSDM)in projection domain for low-dose CT reconstruction. To extract sufficient prior information from single sample, the Hankel matrix formulation is employed. Besides, the penalized weighted least-squares and total variation are introduced to achieve superior image quality. Specifically, we first train a score-based generative model on one sinogram by extracting a great number of tensors from the structural-Hankel matrix as the network input to capture prior distribution. Then, at the inference stage, the stochastic differential equation solver and data consistency step are performed iteratively to obtain the sinogram data. Finally, the final image is obtained through the filtered back-projection algorithm. The reconstructed results are approaching to the normal-dose counterparts. The results prove that OSDM is practical and effective model for reducing the artifacts and preserving the image quality.

低剂量计算机体层摄影(CT)在减少死亡率方面发挥着重要作用 临床应用中的辐射风险。然而，降低辐射剂量 会显著降低图像质量。随着中国经济的快速发展和 深度学习的广泛应用，为我们的学习研究带来了新的方向 低剂量CT成像算法的发展。因此，我们提出了一个全面的 低剂量投影域无监督单样扩散模型(OSDM) CT重建。为了从单个样本中提取足够的先验信息， 采用Hankel矩阵形式。此外，受罚的权重 引入最小二乘和全变差来获得更好的图像。 质量。具体地说，我们首先训练一个基于分数的生成模型 从结构Hankel中提取大量张量的正弦图 矩阵作为网络输入，捕获先验分布。然后，在 推理阶段，随机微分方程解和数据 迭代地执行一致性步骤以获得正弦图数据。 最后，通过滤波反投影得到最终图像 算法。重建结果接近正常剂量。 对口单位。结果表明，OSDM模型是一种实用有效的建模方法 减少伪影，保持图像质量。