readme: # CascadeNet-fastmri
  ---
  tags:
  - TensorFlow
  - MRI reconstruction
  - MRI
  datasets:
  - fastMRI
  ---
  
  This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
  
  ## Model description
  For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
  This section is WIP.
  
  ## Intended uses and limitations
  This model can be used to reconstruct single coil knee data from Siemens scanner at acceleration factor 4.
  It cannot be used on multi-coil data.
  
  ## How to use
  This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
  After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
  The framework is TensorFlow.
  
  You can initialize and load the model weights as follows:
  ```python
  from fastmri_recon.models.functional_models.cascading import cascade_net
  
  model = cascade_net()
  model.load_weights('model_weights.h5')
  ```
  
  Using the model is then as simple as:
  ```python
  model([
      kspace,  # shape: [n_slices, n_rows, n_cols, 1]
      mask,  # shape: [n_slices, n_rows, n_cols]
  ])
  ```
  
  ## Limitations and bias
  The limitations and bias of this model have not been properly investigated.
  
  ## Training data
  This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).
  
  ## Training procedure
  The training procedure is described in https://www.mdpi.com/2076-3417/10/5/1816 for brain data.
  This section is WIP.
  
  ## Evaluation results
  This model was evaluated using the [fastMRI dataset](https://fastmri.org/dataset/).
  
  | Contrast | PD    | PDFS   |
  |----------|-------|--------|
  | PSNR     | 33.98  | 29.88  |
  | SSIM     | 0.811 | 0.6251 |
  
  
  ## Bibtex entry
  ```
  @article{ramzi2020benchmarking,
    title={Benchmarking MRI reconstruction neural networks on large public datasets},
    author={Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
    journal={Applied Sciences},
    volume={10},
    number={5},
    pages={1816},
    year={2020},
    publisher={Multidisciplinary Digital Publishing Institute}
  }
  ```
  
  