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TGDOF

# TGDOF This is the testing code of TGDOF for CS-MRI. Running the script "AddPath" and then the "Demo_TGDOF" to test the basic deep framework for CS-MRI. TestData ------------ The testing MR slices used in experiments, including 25 T1-weighted data and 25 T2-weighted data. The slices are extracted from the subset of the IXI datasets: http://brain-development.org/ixi-dataset/ ArtifactsModel ------------ The pre-trained model used in Module \mathcal{N}. SamplingPatter: ------------ The three kinds of sampling patterns at five different sampling ratios (10% to 50%). If you utilize this code, please cite the related paper: <br> @inproceedings{liu2019theoretically,<br> title={A theoretically guaranteed deep optimization framework for robust compressive sensing mri},<br> author={Liu, Risheng and Zhang, Yuxi and Cheng, Shichao and Fan, Xin and Luo, Zhongxuan},<br> booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},<br> volume={33},<br> pages={4368--4375},<br> year={2019} }

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/learn @dlut-dimt/TGDOF
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0/100

Supported Platforms

Universal

README

TGDOF

This is the testing code of TGDOF for CS-MRI.

Running the script "AddPath" and then the "Demo_TGDOF" to test the basic deep framework for CS-MRI.

TestData

The testing MR slices used in experiments, including 25 T1-weighted data and 25 T2-weighted data. The slices are extracted from the subset of the IXI datasets: http://brain-development.org/ixi-dataset/

ArtifactsModel

The pre-trained model used in Module \mathcal{N}.

SamplingPatter:

The three kinds of sampling patterns at five different sampling ratios (10% to 50%).

If you utilize this code, please cite the related paper: <br> @inproceedings{liu2019theoretically,<br> title={A theoretically guaranteed deep optimization framework for robust compressive sensing mri},<br> author={Liu, Risheng and Zhang, Yuxi and Cheng, Shichao and Fan, Xin and Luo, Zhongxuan},<br> booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},<br> volume={33},<br> pages={4368--4375},<br> year={2019} }

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GitHub Stars6
CategoryDevelopment
Updated8d ago
Forks2

Languages

MATLAB

Security Score

70/100

Audited on Mar 20, 2026

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