DRONE: Dual-domain residual-based optimization network for sparse-view CT reconstruction
Deep learning has attracted rapidly increasing attention in the field of tomographic image
reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among …
reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among …
CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising
Objective. Low-dose computed tomography (LDCT) denoising is an important problem in CT
research. Compared to the normal dose CT, LDCT images are subjected to severe noise …
research. Compared to the normal dose CT, LDCT images are subjected to severe noise …
Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation
Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for
clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably …
clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably …
A review of deep learning ct reconstruction from incomplete projection data
Computed tomography (CT) is a widely used imaging technique in both medical and
industrial applications. However, accurate CT reconstruction requires complete projection …
industrial applications. However, accurate CT reconstruction requires complete projection …
Deep embedding-attention-refinement for sparse-view CT reconstruction
Tomographic image reconstruction with deep learning is an emerging field of applied
artificial intelligence. Reducing radiation dose with sparse views' reconstruction is a …
artificial intelligence. Reducing radiation dose with sparse views' reconstruction is a …
Low-dose CT image synthesis for domain adaptation imaging using a generative adversarial network with noise encoding transfer learning
M Li, J Wang, Y Chen, Y Tang, Z Wu… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Deep learning (DL) based image processing methods have been successfully applied to
low-dose x-ray images based on the assumption that the feature distribution of the training …
low-dose x-ray images based on the assumption that the feature distribution of the training …
Noise characteristics modeled unsupervised network for robust CT image reconstruction
Deep learning (DL)-based methods show great potential in computed tomography (CT)
imaging field. The DL-based reconstruction methods are usually evaluated on the training …
imaging field. The DL-based reconstruction methods are usually evaluated on the training …
CCN-CL: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising
Y Tang, Q Du, J Wang, Z Wu, Y Li, M Li, X Yang… - Computers in Biology …, 2022 - Elsevier
In recent years, low-dose computed tomography (LDCT) has played an increasingly
important role in the diagnosis CT to reduce the potential adverse effects of x-ray radiation …
important role in the diagnosis CT to reduce the potential adverse effects of x-ray radiation …
Domain‐adaptive denoising network for low‐dose CT via noise estimation and transfer learning
J Wang, Y Tang, Z Wu, BMW Tsui, W Chen… - Medical …, 2023 - Wiley Online Library
Background In recent years, low‐dose computed tomography (LDCT) has played an
important role in the diagnosis CT to reduce the potential adverse effects of X‐ray radiation …
important role in the diagnosis CT to reduce the potential adverse effects of X‐ray radiation …
Self-supervised deep learning for joint 3D low-dose PET/CT image denoising
F Zhao, D Li, R Luo, M Liu, X Jiang, J Hu - Computers in Biology and …, 2023 - Elsevier
Deep learning (DL)-based denoising of low-dose positron emission tomography (LDPET)
and low-dose computed tomography (LDCT) has been widely explored. However, previous …
and low-dose computed tomography (LDCT) has been widely explored. However, previous …