Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Congbo Cai

Congbo Cai
Alert button

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning


Oct 19, 2022
Haitao Huang, Qinqin Yang, Jiechao Wang, Pujie Zhang, Shuhui Cai, Congbo Cai

Add code
Alert button

Figure 1 for High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
Figure 2 for High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
Figure 3 for High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
Figure 4 for High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
* 20 pages, 8 figures 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance


Mar 22, 2022
Qinqin Yang, Zi Wang, Kunyuan Guo, Congbo Cai, Xiaobo Qu

Add code
Alert button

Figure 1 for Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance
Figure 2 for Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance
Figure 3 for Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance
Figure 4 for Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition


Jul 30, 2021
Qinqin Yang, Jiechao Wang, Jianfeng Bao, Xiaoyin Wang, Lingceng Ma, Qizhi Yang, Shuhui Cai, Hongjian He, Congbo Cai, Jiyang Dong, Zhong Chen, Jianhui Zhong

Add code
Alert button

Figure 1 for Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition
Figure 2 for Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition
Figure 3 for Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition
Figure 4 for Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition
* 12 pages, 12 figures 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI


Apr 04, 2018
Zhiwen Fan, Liyan Sun, Xinghao Ding, Yue Huang, Congbo Cai, John Paisley

Add code
Alert button

Figure 1 for A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI
Figure 2 for A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI
Figure 3 for A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI
Figure 4 for A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

A Divide-and-Conquer Approach to Compressed Sensing MRI


Mar 27, 2018
Liyan Sun, Zhiwen Fan, Xinghao Ding, Congbo Cai, Yue Huang, John Paisley

Add code
Alert button

Figure 1 for A Divide-and-Conquer Approach to Compressed Sensing MRI
Figure 2 for A Divide-and-Conquer Approach to Compressed Sensing MRI
Figure 3 for A Divide-and-Conquer Approach to Compressed Sensing MRI
Figure 4 for A Divide-and-Conquer Approach to Compressed Sensing MRI
* 37 pages, 20 figures, 2 tables 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

High Efficient Reconstruction of Single-shot T2 Mapping from OverLapping-Echo Detachment Planar Imaging Based on Deep Residual Network


Aug 17, 2017
Congbo Cai, Yiqing Zeng, Chao Wang, Shuhui Cai, Jun Zhang, Zhong Chen, Xinghao Ding, Jianhui Zhong

Add code
Alert button

Figure 1 for High Efficient Reconstruction of Single-shot T2 Mapping from OverLapping-Echo Detachment Planar Imaging Based on Deep Residual Network
Figure 2 for High Efficient Reconstruction of Single-shot T2 Mapping from OverLapping-Echo Detachment Planar Imaging Based on Deep Residual Network
Figure 3 for High Efficient Reconstruction of Single-shot T2 Mapping from OverLapping-Echo Detachment Planar Imaging Based on Deep Residual Network
Figure 4 for High Efficient Reconstruction of Single-shot T2 Mapping from OverLapping-Echo Detachment Planar Imaging Based on Deep Residual Network
* 18 pages, 7 figures 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email