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# KIKI-net-OASIS |
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--- |
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tags: |
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- TensorFlow |
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- MRI reconstruction |
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- MRI |
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datasets: |
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- OASIS |
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--- |
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This model can be used to reconstruct single coil OASIS data with an acceleration factor of 4. |
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## Model description |
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For more details, see https://www.mdpi.com/2076-3417/10/5/1816. |
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This section is WIP. |
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## Intended uses and limitations |
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This model can be used to reconstruct single coil brain retrospective data from the OASIS database at acceleration factor 4. |
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It cannot be used on multi-coil data. |
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## How to use |
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This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark. |
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After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`. |
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The framework is TensorFlow. |
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You can initialize and load the model weights as follows: |
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```python |
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from fastmri_recon.models.functional_models.kiki_sep import full_kiki_net |
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from fastmri_recon.models.utils.non_linearities import lrelu |
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model = full_kiki_net(n_convs=16, n_filters=48, activation=lrelu) |
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model.load_weights('model_weights.h5') |
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``` |
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Using the model is then as simple as: |
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```python |
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model([ |
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kspace, # shape: [n_slices, n_rows, n_cols, 1] |
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mask, # shape: [n_slices, n_rows, n_cols] |
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]) |
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``` |
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## Limitations and bias |
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The limitations and bias of this model have not been properly investigated. |
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## Training data |
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This model was trained using the [OASIS dataset](https://www.oasis-brains.org/). |
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## Training procedure |
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The training procedure is described in https://www.mdpi.com/2076-3417/10/5/1816 for brain data. |
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This section is WIP. |
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## Evaluation results |
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This model was evaluated using the [OASIS dataset](https://www.oasis-brains.org/). |
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- PSNR: 30.08 |
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- SSIM: 0.853 |
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## Bibtex entry |
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``` |
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@article{ramzi2020benchmarking, |
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title={Benchmarking MRI reconstruction neural networks on large public datasets}, |
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author={Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc}, |
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journal={Applied Sciences}, |
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volume={10}, |
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number={5}, |
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pages={1816}, |
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year={2020}, |
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publisher={Multidisciplinary Digital Publishing Institute} |
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} |
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``` |
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