UPDNet-knee-af8

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('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}
}