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 …
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 …
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 …
Structure-preserved meta-learning uniting network for improving low-dose CT quality
Objective. Deep neural network (DNN) based methods have shown promising performances
for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based …
for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based …
CT reconstruction with PDF: Parameter-dependent framework for data from multiple geometries and dose levels
W Xia, Z Lu, Y Huang, Y Liu, H Chen… - … on Medical Imaging, 2021 - ieeexplore.ieee.org
The current mainstream computed tomography (CT) reconstruction methods based on deep
learning usually need to fix the scanning geometry and dose level, which significantly …
learning usually need to fix the scanning geometry and dose level, which significantly …
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 …
Computationally efficient deep neural network for computed tomography image reconstruction
Purpose Deep neural network‐based image reconstruction has demonstrated promising
performance in medical imaging for undersampled and low‐dose scenarios. However, it …
performance in medical imaging for undersampled and low‐dose scenarios. However, it …
Low dose abdominal CT image reconstruction: an unsupervised learning based approach
In medical practice, the X-ray Computed tomography-based scans expose a high radiation
dose and lead to the risk of prostate or abdomen cancers. On the other hand, the low-dose …
dose and lead to the risk of prostate or abdomen cancers. On the other hand, the low-dose …
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 …