Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction
This addresses the memory and scalability challenges in deep learning-based sinogram processing for sparse-view CT, which is crucial for reducing radiation dose in medical imaging, though it is an incremental improvement over existing methods.
The paper tackles sparse-view CT reconstruction by proposing a patch-based denoising diffusion probabilistic model (DDPM) that inpaints down-sampled projection data using unsupervised learning, effectively suppressing artifacts and preserving textural details.
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.