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# 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}
}
```
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