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 …
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 …
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 …
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 …
[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 …
Low‐dose CT reconstruction with Noise2Noise network and testing‐time fine‐tuning
Purpose Deep learning‐based image denoising and reconstruction methods demonstrated
promising performance on low‐dose CT imaging in recent years. However, most existing …
promising performance on low‐dose CT imaging in recent years. However, most existing …
Noise-generating and imaging mechanism inspired implicit regularization learning network for low dose ct reconstrution
Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning
while maintaining image quality, which involves a consistent pursuit of lower incident rays …
while maintaining image quality, which involves a consistent pursuit of lower incident rays …
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 …
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 noise distribution learning for low-dose CT restoration
Fully supervised deep learning (DL) methods have been widely used in low-dose CT
(LDCT) imaging field and can usually achieve high accuracy results. These methods require …
(LDCT) imaging field and can usually achieve high accuracy results. These methods require …