# UNet-fastmri
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---

This model can be used to reconstruct single coil fastMRI 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 knee data from Siemens scanner 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.unet import unet


model = unet(n_layers=4, layers_n_channels=[16, 32, 64, 128], layers_n_non_lins=2,)
model.load_weights('UNet-fastmri/model_weights.h5')

```

Using the model is then as simple as:
```python
model(zero_filled_recon)
```

## 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://www.mdpi.com/2076-3417/10/5/1816 for brain data.
This section is WIP.

## Evaluation results
This model was evaluated using the [fastMRI dataset](https://fastmri.org/dataset/).

| Contrast | PD    | PDFS   |
|----------|-------|--------|
| PSNR     | 33.64  | 29.89  |
| SSIM     | 0.807 | 0.6334 |


## 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}
}
```


{}
region:us
TAGS
#region-us 

UNet-fastmri
============




---


tags:


* TensorFlow
* MRI reconstruction
* MRI
datasets:
* fastMRI




---


This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.


Model description
-----------------


For more details, see URL
This section is WIP.


Intended uses and limitations
-----------------------------


This model can be used to reconstruct single coil knee data from Siemens scanner at acceleration factor 4.
It cannot be used on multi-coil data.


How to use
----------


This model can be loaded using the following repo: URL
After cloning the repo, 'git clone URL 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:


Using the model is then as simple as:


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.


Training procedure
------------------


The training procedure is described in URL for brain data.
This section is WIP.


Evaluation results
------------------


This model was evaluated using the fastMRI dataset.


Contrast: PSNR, PD: 33.64, PDFS: 29.89
Contrast: SSIM, PD: 0.807, PDFS: 0.6334


Bibtex entry
------------



TAGS
#region-us 

passage: TAGS
#region-us 
