Generative modeling in sinogram domain for sparse-view CT reconstruction

B Guan, C Yang, L Zhang, S Niu… - … on Radiation and …, 2023 - ieeexplore.ieee.org
The radiation dose in computed tomography (CT) examinations is harmful for patients but
can be significantly reduced by intuitively decreasing the number of projection views …

[HTML][HTML] Attention-based dual-branch deep network for sparse-view computed tomography image reconstruction

X Gao, T Su, Y Zhang, J Zhu, Y Tan, H Cui… - … Imaging in Medicine …, 2023 - ncbi.nlm.nih.gov
Background The widespread application of X-ray computed tomography (CT) imaging in
medical screening makes radiation safety a major concern for public health. Sparse-view CT …

Sparse-view CT reconstruction via generative adversarial networks

Z Zhao, Y Sun, P Cong - 2018 IEEE Nuclear Science …, 2018 - ieeexplore.ieee.org
Low dose and sparse view CT are effective approaches to reduce the radiation dose and
accelerate scan speed. Images reconstructed from insufficient data acquired from low dose …

PARS-NET: a novel deep learning framework using parallel residual conventional neural networks for sparse-view CT reconstruction

H Khodajou-Chokami, SA Hosseini… - Journal of …, 2022 - iopscience.iop.org
Sparse-view computed tomography (CT) is recently proposed as a promising method to
speed up data acquisition and alleviate the issue of CT high dose delivery to the patients …

A lightweight dual-domain attention framework for sparse-view CT reconstruction

C Sun, K Deng, Y Liu, H Yang - 2022 IEEE 8th International …, 2022 - ieeexplore.ieee.org
Computed tomography (CT) plays an essential role in clinical diagnosis. Sparse sampling is
an effective way to reduce the radiation on patients, but it will lead to severe artifacts on the …

Patch-based denoising diffusion probabilistic model for sparse-view CT reconstruction

W Xia, W Cong, G Wang - arXiv preprint arXiv:2211.10388, 2022 - arxiv.org
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 …

Learning to distill global representation for sparse-view CT

Z Li, C Ma, J Chen, J Zhang… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Sparse-view computed tomography (CT)---using a small number of projections for
tomographic reconstruction---enables much lower radiation dose to patients and …

LRR-CED: Low-resolution reconstruction-aware convolutional encoder–decoder network for direct sparse-view CT image reconstruction

VSS Kandarpa, A Perelli, A Bousse… - Physics in Medicine & …, 2022 - iopscience.iop.org
Objective. Sparse-view computed tomography (CT) reconstruction has been at the forefront
of research in medical imaging. Reducing the total x-ray radiation dose to the patient while …

Sparsier2Sparse: Self‐supervised convolutional neural network‐based streak artifacts reduction in sparse‐view CT images

S Kim, B Kim, J Lee, J Baek - Medical physics, 2023 - Wiley Online Library
Background Sparse‐view computed tomography (CT) has attracted a lot of attention for
reducing both scanning time and radiation dose. However, sparsely‐sampled projection …

Comparison of projection domain, image domain, and comprehensive deep learning for sparse-view X-ray CT image reconstruction

K Liang, H Yang, Y Xing - arXiv preprint arXiv:1804.04289, 2018 - arxiv.org
X-ray Computed Tomography (CT) imaging has been widely used in clinical diagnosis, non-
destructive examination, and public safety inspection. Sparse-view (sparse view) CT has …