Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction

• URL: http://arxiv.org/abs/2211.10388v1
• Date: Fri, 18 Nov 2022 17:35:36 GMT
• Title: Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction
• Authors: Wenjun Xia, Wenxiang Cong, Ge Wang
• Abstract summary: Sparse-view computed tomography (CT) can be used to reduce radiation dose greatly but suffers from severe image artifacts. Deep learning based method for sparse-view CT reconstruction has attracted a major attention. We propose a patch-based denoising diffusion probabilistic model (DDPM) for sparse-view CT reconstruction.
• Score: 6.907847093036819
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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