FTVNNR_Dynamic_MRI

This contains source codes of MedIA18 paper

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An efficient algorithm for dynamic MRI using low-rank and total variation regularizations

By Jiawen Yao, Zheng Xu, Xiaolei Huang, Junzhou Huang, In Medical image analysis, Vol. 44, 14-27, 2018

FTVNNR was initially published in MICCAI 2015 .

Contents

1. Requirements: software
2. Demo

Requirements: software

• MATLAB
• A toolbox for compressive sensing (CS) magnetic resonance imaging (MRI) with structured sparsity [MATLAB Code]
  • I have already integrated this toolbox here. But please consider citing the above work when using in your research.

Demo

Run the demo_FTVNNR.m and you will see the following results:

If you find FTVNNR useful in your research, please consider citing:

@article{yao2018efficient,
  title={An efficient algorithm for dynamic MRI using low-rank and total variation regularizations},
  author={Yao, Jiawen and Xu, Zheng and Huang, Xiaolei and Huang, Junzhou},
  journal={Medical image analysis},
  volume={44},
  pages={14--27},
  year={2018},
  publisher={Elsevier}
}

@inproceedings{yao2015accelerated,
  title={Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization},
  author={Yao, Jiawen and Xu, Zheng and Huang, Xiaolei and Huang, Junzhou},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={635--642},
  year={2015},
  organization={Springer}
}