Progressive transfer learning strategy for low-dose CT image reconstruction with limited annotated data
Low-dose computed tomography (LDCT) examinations are of essential usages in clinical
applications due to the lower radiation-associated cancer risks in CT imaging. Reductions in …
applications due to the lower radiation-associated cancer risks in CT imaging. Reductions in …
LdCT-Net: low-dose CT image reconstruction strategy driven by a deep dual network
High radiation dose in CT imaging is a major concern, which could result in increased
lifetime risk of cancers. Therefore, to reduce the radiation dose at the same time maintaining …
lifetime risk of cancers. Therefore, to reduce the radiation dose at the same time maintaining …
Deep neural networks for low-dose CT image reconstruction via cooperative meta-learning strategy
Recently, deep neural networks (DNNs) have been widely applied in low-dose computed
tomography (LDCT) imaging field. Their performances are highly related to the number of …
tomography (LDCT) imaging field. Their performances are highly related to the number of …
Teacher-student network for CT image reconstruction via meta-learning strategy
Deep neural networks (DNN) have been widely used in computed tomography (CT)
imaging, with promising performance. Meanwhile, most of them are supervised learning …
imaging, with promising performance. Meanwhile, most of them are supervised learning …
Noise-conscious explicit weighting network for robust low-dose CT imaging
Supervised deep learning (DL) methods have been widely developed to remove noise-
induced artifacts and promote image quality in the low-dose CT imaging task via good …
induced artifacts and promote image quality in the low-dose CT imaging task via good …
Semi-supervised learned sinogram restoration network for low-dose CT image reconstruction
With the development of deep learning (DL), many deep learning (DL) based algorithms
have been widely used in the low-dose CT imaging and achieved promising reconstruction …
have been widely used in the low-dose CT imaging and achieved promising reconstruction …
AIGAN: Attention–encoding Integrated Generative Adversarial Network for the reconstruction of low-dose CT and low-dose PET images
X-ray computed tomography (CT) and positron emission tomography (PET) are two of the
most commonly used medical imaging technologies for the evaluation of many diseases …
most commonly used medical imaging technologies for the evaluation of many diseases …
A model-based unsupervised deep learning method for low-dose CT reconstruction
Low-dose CT (LDCT) is of great significance due to the concern about the potential radiation
risk. With the fast development of deep learning, neural networks have become powerful …
risk. With the fast development of deep learning, neural networks have become powerful …
[HTML][HTML] Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network
Q Li, S Li, R Li, W Wu, Y Dong, J Zhao… - … Imaging in Medicine …, 2022 - ncbi.nlm.nih.gov
Background Computed tomography (CT) is widely used in medical diagnoses due to its
ability to non-invasively detect the internal structures of the human body. However, CT scans …
ability to non-invasively detect the internal structures of the human body. However, CT scans …
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 …