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 …
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 …
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 …
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 …
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 …
DPDudoNet: Deep-Prior Based Dual-Domain Network for Low-Dose Computed Tomography
TEG Komolafe, Y Cao, D Sun, N Shen… - Machine Learning for …, 2022 - books.google.com
Low-dose computed tomography (LDCT) reconstruction has been an active research field
for years. Although deep learning (DL)-based methods have achieved incredible success in …
for years. Although deep learning (DL)-based methods have achieved incredible success in …
[PDF][PDF] Synergizing physics/model-based and data-driven methods for low-dose CT
Abstract Since 2016, deep learning (DL) has advanced tomographic imaging with
remarkable successes, especially in low-dose computed tomography (LDCT) imaging …
remarkable successes, especially in low-dose computed tomography (LDCT) imaging …
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 …