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:
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('UPDNet-knee-af8/model_weights.h5')
Using the model is then as simple as:
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.
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}
}