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# NCPDNet-multicoil-radial
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This is a non-Cartesian multicoil MRI reconstruction model for radial trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see https://hal.inria.fr/hal-03188997.
This section is WIP.
## Intended uses and limitations
This model can be used to reconstruct multicoil knee data from Siemens scanner at acceleration factor 4 in a radial acquisition setting.
## 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.ncpdnet import NCPDNet
model = NCPDNet(
multicoil=True,
im_size=(640, 400),
dcomp=True,
refine_smaps=True,
)
kspace_shape = 1
inputs = [
tf.zeros([1, 1, kspace_shape, 1], dtype=tf.complex64),
tf.zeros([1, 2, kspace_shape], dtype=tf.float32),
tf.zeros([1, 1, 640, 320], dtype=tf.complex64),
(tf.constant([320]), tf.ones([1, kspace_shape], dtype=tf.float32)),
]
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_kspace_samples, 1]
traj, # shape: [n_slices, n_coils, 2, n_kspace_samples]
smaps, # shape: [n_slices, n_coils, n_kspace_samples, n_coils]
(
output_shape, # shape: [n_slices, 1]
dcomp, # shape: [n_slices, n_kspace_samples]
)
])
```
## 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://hal.inria.fr/hal-03188997.
This section is WIP.
## Evaluation results
On the fastMRI validation dataset:
- PSNR: 40.00
- SSIM: 0.9191
## Bibtex entry
```
@article{ramzi2022nc,
title={NC-PDNet: A density-compensated unrolled network for 2D and 3D non-Cartesian MRI reconstruction},
author={Ramzi, Zaccharie and Chaithya, GR and Starck, Jean-Luc and Ciuciu, Philippe},
journal={IEEE Transactions on Medical Imaging},
volume={41},
number={7},
pages={1625--1638},
year={2022},
publisher={IEEE}
}
```
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