readme: # UPDNet-knee-singlecoil-af4
  ---
  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://arxiv.org/abs/2010.07290.
  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
  import tensorflow as tf
  
  from fastmri_recon.models.subclassed_models.updnet import UPDNet
  
  
  model = UPDNet(
      n_dual=1,
      primal_only=True,
      layers_n_channels=[16 * 2**i for i in range(3)],
  )
  kspace_size = [1, 320, 320]
  inputs = [
      tf.zeros(kspace_size + [1], dtype=tf.complex64),  # kspace
      tf.zeros(kspace_size, dtype=tf.complex64),  # mask
  ]
  model(inputs)
  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://arxiv.org/abs/2010.07290 for brain data.
  This section is WIP.
  
  ## Evaluation results
  Not available
  
  
  ## Bibtex entry
  ```
  @inproceedings{Ramzi2020d,
  archivePrefix = {arXiv},
  arxivId = {2010.07290},
  author = {Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
  booktitle = {ISMRM},
  eprint = {2010.07290},
  pages = {1--4},
  title = {{XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge}},
  url = {http://arxiv.org/abs/2010.07290},
  year = {2021}
  }
  ```
  
  