File size: 3,653 Bytes
7a4f625 1658c98 8c5f907 1658c98 7a4f625 9e2d6b9 7a4f625 9e2d6b9 7a4f625 1658c98 7a4f625 1658c98 845a94e 1658c98 845a94e 0680a66 845a94e 1658c98 9e2d6b9 1658c98 aa40f73 7a4f625 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
# XPDNet-brain-af4
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model was used to achieve the 3rd highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/).
It is a base model for acceleration factor 4.
The model uses 25 iterations and a medium MWCNN, and a big 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 brain data from Siemens scanner at acceleration factor 4.
It was shown [here](https://arxiv.org/abs/2106.00753), that it can generalize well, although further tests are required.
## 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:
```python
import tensorflow as tf
from fastmri_recon.models.subclassed_models.denoisers.proposed_params import get_model_specs
from fastmri_recon.models.subclassed_models.xpdnet import XPDNet
n_primal = 5
model_fun, model_kwargs, n_scales, res = [
(model_fun, kwargs, n_scales, res)
for m_name, m_size, model_fun, kwargs, _, n_scales, res in get_model_specs(n_primal=n_primal, force_res=False)
if m_name == 'MWCNN' and m_size == 'medium'
][0]
model_kwargs['use_bias'] = False
run_params = dict(
n_primal=n_primal,
multicoil=True,
n_scales=n_scales,
refine_smaps=True,
refine_big=True,
res=res,
output_shape_spec=True,
n_iter=25,
)
model = XPDNet(model_fun, model_kwargs, **run_params)
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
tf.constant([[320, 320]]), # shape
]
model(inputs)
model.load_weights('model_weights.h5')
```
Using the model is then as simple as:
```python
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]
shape, # shape: [n_slices, 2]
])
```
## 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](https://fastmri.org/dataset/).
## Training procedure
The training procedure is described in https://arxiv.org/abs/2010.07290.
This section is WIP.
## Evaluation results
On the fastMRI validation dataset, the same model with a smaller sensitivity maps refiner gives the following results for 30 validation volumes per contrast:
| Contrast | T1 | T2 | FLAIR | T1-POST |
|----------|--------|--------|--------|---------|
| PSNR | 41.56 | 40.68 | 39.60 | 42.53 |
| SSIM | 0.9506 | 0.9554 | 0.9321 | 0.9683 |
Further results can be seen on the fastMRI leaderboards for the test and challenge dataset: https://fastmri.org/leaderboards/
## 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}
}
```
|