# Source code for monai.apps.reconstruction.networks.nets.varnet

```
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import copy
import torch.nn as nn
from torch import Tensor
from monai.apps.reconstruction.complex_utils import complex_abs_t
from monai.apps.reconstruction.mri_utils import root_sum_of_squares_t
from monai.apps.reconstruction.networks.blocks.varnetblock import VarNetBlock
from monai.networks.blocks.fft_utils_t import ifftn_centered_t
[docs]
class VariationalNetworkModel(nn.Module):
"""
The end-to-end variational network (or simply e2e-VarNet) based on Sriram et. al., "End-to-end variational
networks for accelerated MRI reconstruction".
It comprises several cascades each consisting of refinement and data consistency steps. The network takes in
the under-sampled kspace and estimates the ground-truth reconstruction.
Modified and adopted from: https://github.com/facebookresearch/fastMRI
Args:
coil_sensitivity_model: A convolutional model for learning coil sensitivity maps. An example is
:py:class:`monai.apps.reconstruction.networks.nets.coil_sensitivity_model.CoilSensitivityModel`.
refinement_model: A convolutional network used in the refinement step of e2e-VarNet. An example
is :py:class:`monai.apps.reconstruction.networks.nets.complex_unet.ComplexUnet`.
num_cascades: Number of cascades. Each cascade is a
:py:class:`monai.apps.reconstruction.networks.blocks.varnetblock.VarNetBlock` which consists of
refinement and data consistency steps.
spatial_dims: number of spatial dimensions.
"""
def __init__(
self,
coil_sensitivity_model: nn.Module,
refinement_model: nn.Module,
num_cascades: int = 12,
spatial_dims: int = 2,
):
super().__init__()
self.coil_sensitivity_model = coil_sensitivity_model
self.cascades = nn.ModuleList([VarNetBlock(copy.deepcopy(refinement_model)) for i in range(num_cascades)])
self.spatial_dims = spatial_dims
[docs]
def forward(self, masked_kspace: Tensor, mask: Tensor) -> Tensor:
"""
Args:
masked_kspace: The under-sampled kspace. It's a 2D kspace (B,C,H,W,2)
with the last dimension being 2 (for real/imaginary parts) and C denoting the
coil dimension. 3D data will have the shape (B,C,H,W,D,2).
mask: The under-sampling mask with shape (1,1,1,W,1) for 2D data or (1,1,1,1,D,1) for 3D data.
Returns:
The reconstructed image which is the root sum of squares (rss) of the absolute value
of the inverse fourier of the predicted kspace (note that rss combines coil images into one image).
"""
sensitivity_maps = self.coil_sensitivity_model(masked_kspace, mask) # shape is similar to masked_kspace
kspace_pred = masked_kspace.clone()
for cascade in self.cascades:
kspace_pred = cascade(kspace_pred, masked_kspace, mask, sensitivity_maps)
output_image = root_sum_of_squares_t(
complex_abs_t(ifftn_centered_t(kspace_pred, spatial_dims=self.spatial_dims)),
spatial_dim=1, # 1 is for C which is the coil dimension
) # shape is (B,H,W) for 2D and (B,H,W,D) for 3D data.
return output_image
```