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 …
destructive examination, and public safety inspection. Sparse-view (sparse view) CT has …
LRR-CED: Low-resolution reconstruction-aware convolutional encoder–decoder network for direct sparse-view CT image reconstruction
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 …
of research in medical imaging. Reducing the total x-ray radiation dose to the patient while …
[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 …
medical screening makes radiation safety a major concern for public health. Sparse-view CT …
Hybrid-domain neural network processing for sparse-view CT reconstruction
X-ray computed tomography (CT) is one of the most widely used tools in medical imaging,
industrial nondestructive testing, lesion detection, and other applications. However …
industrial nondestructive testing, lesion detection, and other applications. However …
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 …
speed up data acquisition and alleviate the issue of CT high dose delivery to the patients …
An attention-based deep convolutional neural network for ultra-sparse-view CT reconstruction
Abstract X-ray Computed Tomography (CT) techniques play a vitally important role in clinical
diagnosis, but radioactivity exposure can also induce the risk of cancer for patients. Sparse …
diagnosis, but radioactivity exposure can also induce the risk of cancer for patients. Sparse …
Artifact removal using improved GoogLeNet for sparse-view CT reconstruction
Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with
both accelerated scan and reduced projection/back-projection calculation. Despite the rapid …
both accelerated scan and reduced projection/back-projection calculation. Despite the rapid …
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 …
accelerate scan speed. Images reconstructed from insufficient data acquired from low dose …
Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior
Z Shu, A Entezari - Computer Methods and Programs in Biomedicine, 2022 - Elsevier
Background and objective: Neural network based image reconstruction methods are
becoming increasingly popular. However, limited training data and the lack of theoretical …
becoming increasingly popular. However, limited training data and the lack of theoretical …
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 …
can be significantly reduced by intuitively decreasing the number of projection views …