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Projection-Based cascaded U-Net model for MR image reconstruction
Amir Aghabiglou
,
Ender M. Eksioglu
*
*
Corresponding author for this work
Department of Electronics and Communication Engineering
Istanbul Technical University
Research output
:
Contribution to journal
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Article
›
peer-review
26
Citations (Scopus)
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Keyphrases
U-Net
100%
MR Image Reconstruction
100%
Projection-based
100%
Data Consistency
25%
Deep Learning
16%
Reconstruction Performance
16%
Undersampling
16%
MRI Reconstruction
16%
Fast MRI
16%
Deep Network
16%
Updated Data
16%
Network Structure
8%
Magnetic Resonance Imaging
8%
Inverse Problem
8%
K-space
8%
Segmentation Problem
8%
Deep Model
8%
K-Space Data
8%
Reconstruction Method
8%
Cascade Structure
8%
Effective Network
8%
Reconstruction Process
8%
Learning Implementation
8%
Data Acquisition Time
8%
Reconstruction Problem
8%
Imaging Inverse Problems
8%
Cascaded CNN
8%
Magnetic Resonance Image Reconstruction
8%
Aliasing Artifacts
8%
Annotated Dataset
8%
Biomedical Images
8%
PyTorch
8%
Cascade Framework
8%
Engineering
Image Reconstruction
100%
Net Structure
100%
Convolutional Neural Network
100%
Deep Learning Method
66%
Simulation Result
33%
Space Data
33%
Acquisition Time
33%
Reconstruction Problem
33%
Computer Science
U-Net
100%
Image Reconstruction
100%
Convolutional Neural Network
25%
Data Consistency
25%
Inverse Problem
16%
Deep Learning Method
16%
Speed-up
8%
Reconstruction Process
8%
Reconstruction Problem
8%
Aliasing Artifact
8%
Conventional Data
8%
Acquisition Time
8%
Network Structure
8%
Physics
Image Reconstruction
100%
Convolutional Neural Network
100%
Deep Learning Method
66%
Data Acquisition
33%
Magnetic Resonance
33%
Earth and Planetary Sciences
Image Reconstruction
100%
Inverse Problem
66%
Data Acquisition
33%