Sparse-view computed tomography (CT) can be used to reduce radiation dose greatly but is suffers from severe image artifacts. Recently, the deep learning based method for sparse-view CT reconstruction has attracted a major attention. However, neural networks often have a limited ability to remove the artifacts when they only work in the image domain. Deep learning-based sinogram processing can achieve a better anti-artifact performance, but it inevitably requires feature maps of the whole image in a video memory, which makes handling large-scale or three-dimensional (3D) images rather challenging. In this paper, we propose a patch-based denoising diffusion probabilistic model (DDPM) for sparse-view CT reconstruction. A DDPM network based on patches extracted from fully sampled projection data is trained and then used to inpaint down-sampled projection data. The network does not require paired full-sampled and down-sampled data, enabling unsupervised learning. Since the data processing is patch-based, the deep learning workflow can be distributed in parallel, overcoming the memory problem of large-scale data. Our experiments show that the proposed method can effectively suppress few-view artifacts while faithfully preserving textural details.
翻译:光谱计算断层仪表(CT) 可用于大幅降低辐射剂量,但受严重图像文物的影响。 最近, 以深层次学习为基础的稀释光谱重建方法引起了人们的极大关注。 然而, 神经网络在仅仅在图像域工作时, 通常具有有限的去除文物的能力。 深层次学习基础的罪恶图处理可以实现更好的反麻醉性能, 但是这不可避免地需要用视频内存对整个图像的地貌图图, 使得处理大尺度或三维( 3D) 图像具有相当大的挑战性。 在本文中, 我们提议为稀释光谱图组重建使用一个基于深层次学习的分散扩散概率模型( DDPM ) 。 基于完全抽样的投影数据提取的补片的 DDPM 网络经过培训, 并随后用于对下扫描的投影数据。 这个网络不需要配对齐的全版和下版的数据, 从而能够进行不严密的学习。 由于数据处理是基于多层的, 深层学习工作流程可以平行分布在平行的, 克服大型历史细节上的记忆问题。 我们的实验显示, 能够有效地抑制一些方法 。