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git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark, you can install the package via pip install fastmri-reproducible-benchmark.
The framework is TensorFlow.1import tensorflow as tf
2
3from fastmri_recon.models.subclassed_models.ncpdnet import NCPDNet
4
5
6model = NCPDNet(
7 multicoil=True,
8 im_size=(640, 400),
9 dcomp=True,
10 refine_smaps=True,
11)
12kspace_shape = 1
13inputs = [
14 tf.zeros([1, 1, kspace_shape, 1], dtype=tf.complex64),
15 tf.zeros([1, 2, kspace_shape], dtype=tf.float32),
16 tf.zeros([1, 1, 640, 320], dtype=tf.complex64),
17 (tf.constant([320]), tf.ones([1, kspace_shape], dtype=tf.float32)),
18]
19model(inputs)
20model.load_weights('model_weights.h5')1model([
2 kspace, # shape: [n_slices, n_coils, n_kspace_samples, 1]
3 traj, # shape: [n_slices, n_coils, 2, n_kspace_samples]
4 smaps, # shape: [n_slices, n_coils, n_kspace_samples, n_coils]
5 (
6 output_shape, # shape: [n_slices, 1]
7 dcomp, # shape: [n_slices, n_kspace_samples]
8 )
9])@unpublished{ramzi:hal-03188997,
TITLE = {{NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction}},
AUTHOR = {Ramzi, Zaccharie and G R, Chaithya and Starck, Jean-Luc and Ciuciu, Philippe},
YEAR = {2021},
MONTH = Sep,
}