| # KIKI-net-OASIS | |
| --- | |
| tags: | |
| - TensorFlow | |
| - MRI reconstruction | |
| - MRI | |
| datasets: | |
| - OASIS | |
| --- | |
| This model can be used to reconstruct single coil OASIS 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 brain retrospective data from the OASIS database 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.kiki_sep import full_kiki_net | |
| from fastmri_recon.models.utils.non_linearities import lrelu | |
| model = full_kiki_net(n_convs=16, n_filters=48, activation=lrelu) | |
| 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 [OASIS dataset](https://www.oasis-brains.org/). | |
| ## 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 [OASIS dataset](https://www.oasis-brains.org/). | |
| - PSNR: 30.08 | |
| - SSIM: 0.853 | |
| ## 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} | |
| } | |
| ``` | |