| # UPDNet-knee-af8 | |
| --- | |
| tags: | |
| - TensorFlow | |
| - MRI reconstruction | |
| - MRI | |
| datasets: | |
| - fastMRI | |
| --- | |
| This model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/) (0.2dB behind the 2nd submission). | |
| It is a base model for acceleration factor 8. | |
| The model uses 25 iterations and a medium-ca-prelu U-net, and a medium sensitivity maps refiner. | |
| ## 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 knee data from Siemens scanner at acceleration factor 8. | |
| ## 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( | |
| multicoil=True, | |
| n_dual=1, | |
| primal_only=True, | |
| n_layers=4, | |
| n_iter=25, | |
| channel_attention_kwargs={'dense': True}, | |
| refine_smaps=True, | |
| non_linearity='prelu', | |
| layers_n_channels=[16 * 2**i for i in range(4)], | |
| ) | |
| kspace_size = [1, 1, 320, 320] | |
| inputs = [ | |
| tf.zeros(kspace_size + [1], dtype=tf.complex64), # kspace | |
| tf.zeros(kspace_size, dtype=tf.complex64), # mask | |
| tf.zeros(kspace_size, dtype=tf.complex64), # smaps | |
| ] | |
| model(inputs) | |
| model.load_weights('model_weights.h5') | |
| ``` | |
| Using the model is then as simple as: | |
| ```python | |
| model([ | |
| kspace, # shape: [n_slices, n_coils, n_rows, n_cols, 1] | |
| mask, # shape: [n_slices, n_coils, n_rows, n_cols] | |
| smaps, # shape: [n_slices, n_coils, 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. | |
| This section is WIP. | |
| ## Evaluation results | |
| No evaluation available outside the one from the fastMRI leaderboard (id: `updnet_v3`). | |
| ## 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} | |
| } | |
| ``` | |