UPDNet-knee-singlecoil-af4

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}
}