# Source code for monai.apps.reconstruction.networks.blocks.varnetblock

```
# 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 torch
import torch.nn as nn
from torch import Tensor
from monai.apps.reconstruction.networks.nets.utils import sensitivity_map_expand, sensitivity_map_reduce
[docs]class VarNetBlock(nn.Module):
"""
A variational block based on Sriram et. al., "End-to-end variational networks for accelerated MRI reconstruction".
It applies data consistency and refinement to the intermediate kspace and combines those results.
Modified and adopted from: https://github.com/facebookresearch/fastMRI
Args:
refinement_model: the model used for refinement (typically a U-Net but can be any deep learning model
that performs well when the input and output are in image domain (e.g., a convolutional network).
spatial_dims: is 2 for 2D data and is 3 for 3D data
"""
def __init__(self, refinement_model: nn.Module, spatial_dims: int = 2):
super().__init__()
self.model = refinement_model
self.spatial_dims = spatial_dims
self.dc_weight = nn.Parameter(torch.ones(1)) # learned scalar as the multiplier of the DC block
buffer_shape = [1 for _ in range(spatial_dims + 3)] # 3 denotes the batch, channel, and real/complex dimensions
self.register_buffer("zeros", torch.zeros(buffer_shape))
[docs] def soft_dc(self, x: Tensor, ref_kspace: Tensor, mask: Tensor) -> Tensor:
"""
Applies data consistency to input x. Suppose x is an intermediate estimate of the kspace and ref_kspace
is the reference under-sampled measurement. This function returns mask * (x - ref_kspace). View this as the
residual between the original under-sampled kspace and the estimate given by the network.
Args:
x: 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).
ref_kspace: original under-sampled kspace with the same shape as x.
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:
Output of DC block with the same shape as x
"""
return torch.where(mask, x - ref_kspace, self.zeros) * self.dc_weight # type: ignore
[docs] def forward(self, current_kspace: Tensor, ref_kspace: Tensor, mask: Tensor, sens_maps: Tensor) -> Tensor:
"""
Args:
current_kspace: Predicted kspace from the previous block. 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).
ref_kspace: reference kspace for applying data consistency (is the under-sampled kspace in MRI reconstruction).
Its shape is the same as current_kspace.
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.
sens_maps: coil sensitivity maps with the same shape as current_kspace
Returns:
Output of VarNetBlock with the same shape as current_kspace
"""
dc_out = self.soft_dc(current_kspace, ref_kspace, mask) # output of DC block
refinement_out = sensitivity_map_expand(
self.model(sensitivity_map_reduce(current_kspace, sens_maps, spatial_dims=self.spatial_dims)),
sens_maps,
spatial_dims=self.spatial_dims,
) # output of refinement model
output = current_kspace - dc_out - refinement_out
return output
```