| # NCPDNet-multicoil-radial | |
| --- | |
| tags: | |
| - TensorFlow | |
| - MRI reconstruction | |
| - MRI | |
| datasets: | |
| - fastMRI | |
| --- | |
| This is a non-Cartesian multicoil MRI reconstruction model for radial trajectories at acceleration factor 4. | |
| The model uses 10 iterations and a small vanilla CNN. | |
| ## Model description | |
| For more details, see https://hal.inria.fr/hal-03188997. | |
| This section is WIP. | |
| ## Intended uses and limitations | |
| This model can be used to reconstruct multicoil knee data from Siemens scanner at acceleration factor 4 in a radial acquisition setting. | |
| ## 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.ncpdnet import NCPDNet | |
| model = NCPDNet( | |
| multicoil=True, | |
| im_size=(640, 400), | |
| dcomp=True, | |
| refine_smaps=True, | |
| ) | |
| kspace_shape = 1 | |
| inputs = [ | |
| tf.zeros([1, 1, kspace_shape, 1], dtype=tf.complex64), | |
| tf.zeros([1, 2, kspace_shape], dtype=tf.float32), | |
| tf.zeros([1, 1, 640, 320], dtype=tf.complex64), | |
| (tf.constant([320]), tf.ones([1, kspace_shape], dtype=tf.float32)), | |
| ] | |
| 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_kspace_samples, 1] | |
| traj, # shape: [n_slices, n_coils, 2, n_kspace_samples] | |
| smaps, # shape: [n_slices, n_coils, n_kspace_samples, n_coils] | |
| ( | |
| output_shape, # shape: [n_slices, 1] | |
| dcomp, # shape: [n_slices, n_kspace_samples] | |
| ) | |
| ]) | |
| ``` | |
| ## 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://hal.inria.fr/hal-03188997. | |
| This section is WIP. | |
| ## Evaluation results | |
| On the fastMRI validation dataset: | |
| - PSNR: 40.00 | |
| - SSIM: 0.9191 | |
| ## Bibtex entry | |
| ``` | |
| @article{ramzi2022nc, | |
| title={NC-PDNet: A density-compensated unrolled network for 2D and 3D non-Cartesian MRI reconstruction}, | |
| author={Ramzi, Zaccharie and Chaithya, GR and Starck, Jean-Luc and Ciuciu, Philippe}, | |
| journal={IEEE Transactions on Medical Imaging}, | |
| volume={41}, | |
| number={7}, | |
| pages={1625--1638}, | |
| year={2022}, | |
| publisher={IEEE} | |
| } | |
| ``` | |