CascadeNet-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:
from fastmri_recon.models.functional_models.cascading import cascade_net
model = cascade_net()
model.load_weights('model_weights.h5')
Using the model is then as simple as:
model([
kspace, # shape: [n_slices, n_rows, n_cols, 1]
mask, # shape: [n_slices, 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.
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.
Contrast | PD | PDFS |
---|---|---|
PSNR | 33.98 | 29.88 |
SSIM | 0.811 | 0.6251 |
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}
}