UPDNet-knee-singlecoil-af4
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://arxiv.org/abs/2010.07290. 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:
import tensorflow as tf
from fastmri_recon.models.subclassed_models.updnet import UPDNet
model = UPDNet(
n_dual=1,
primal_only=True,
layers_n_channels=[16 * 2**i for i in range(3)],
)
kspace_size = [1, 320, 320]
inputs = [
tf.zeros(kspace_size + [1], dtype=tf.complex64), # kspace
tf.zeros(kspace_size, dtype=tf.complex64), # mask
]
model(inputs)
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://arxiv.org/abs/2010.07290 for brain data. This section is WIP.
Evaluation results
Not available
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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