论文标题
低剂量CT成像的投影域中的一个样品扩散模型
One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging
论文作者
论文摘要
低剂量计算机断层扫描（CT）在降低临床应用中的辐射风险中起着重要作用。但是，降低辐射剂量将显着降低图像质量。随着深度学习的快速发展和广泛的应用，它为开发低剂量CT成像算法带来了新的方向。因此，我们提出了一个完全无监督的样品扩散模型（OSDM），用于低剂量CT重建。为了从单个样本中提取足够的先验信息，采用了Hankel矩阵公式。此外，引入了受惩罚的加权最小二乘和总变化，以实现出色的图像质量。具体而言，我们首先通过从结构 - 汉克尔基质中提取大量张量作为网络输入来捕获先前分布的网络来训练基于得分的生成模型。然后，在推论阶段，进行随机微分方程求解器和数据一致性步骤进行迭代以获取Sinogram数据。最后，通过过滤后的反射算法获得最终图像。重建的结果接近正常剂量对应物。结果证明，OSDM是减少工件并保留图像质量的实用和有效模型。
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.
