An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction
AVP Sarvari, K Sridevi - Biomedical Signal Processing and Control, 2023 - Elsevier
COVID-19 has spread all over the world, causing serious panic around the globe. Chest
computed tomography (CT) images are integral in confirming COVID positive patients …
computed tomography (CT) images are integral in confirming COVID positive patients …
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 …
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 …
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 …
Computational-efficient cascaded neural network for CT image reconstruction
In computed tomographic (CT) image reconstruction, image prior design and parameter
tuning are important to improving the image reconstruction quality from noisy or …
tuning are important to improving the image reconstruction quality from noisy or …
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 …
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 …
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 …
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 …
[HTML][HTML] The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
reconstruction (IR), which have been utilised widely in the image reconstruction process of …