  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.
