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DFUSNN: zero-shot dual-domain fusion unsupervised neural network for parallel MRI reconstruction
Shengyi Chen1, Jizhong Duan1, Xinmin Ren1
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, People's Republic of China.
Physics in Medicine and Biology
|April 11, 2024
Summary
A new zero-shot dual-domain fusion unsupervised neural network (DFUSNN) reconstructs parallel magnetic resonance (MR) images without external training data. This method achieves high-quality MR image reconstruction, overcoming limitations of current deep learning approaches.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Signal Processing
Background:
- Deep learning models excel at reconstructing parallel magnetic resonance (MR) images from undersampled k-space data.
- Current methods often require large datasets of fully sampled MR data for training, which are difficult to obtain in many clinical scenarios.
Purpose of the Study:
- To develop an effective unsupervised deep learning method for parallel MR imaging reconstruction that eliminates the need for external training datasets.
- To improve MR image reconstruction quality in situations where acquiring fully sampled data is challenging.
Main Methods:
- Introduction of a novel zero-shot dual-domain fusion unsupervised neural network (DFUSNN).
- Utilized the Noise2Noise (N2N) network in the k-space domain, incorporating phase and coil sensitivity smoothness priors.
- Employed an early stopping criterion to prevent overfitting and Bayesian optimization for dual-domain fusion.
Main Results:
- The DFUSNN demonstrated superior performance compared to existing unsupervised methods and the Hankel-k-space generative model (HKGM) in simulations.
- Achieved reconstruction quality comparable to supervised methods like Deep-SLR.
- Validated across three datasets with varying undersampling patterns.
Conclusions:
- The DFUSNN provides a robust and effective solution for high-quality parallel MR image reconstruction.
- Successfully overcomes the dependency on large external training datasets, making it suitable for data-scarce scenarios.
- Presents a significant advancement in unsupervised deep learning for medical imaging.
