# coding=utf-8
__author__ = "Dimitrios Karkalousos"
from abc import ABC
import torch
from omegaconf import DictConfig, OmegaConf
from pytorch_lightning import Trainer
from torch.nn import L1Loss
from mridc.collections.common.losses.ssim import SSIMLoss
from mridc.collections.common.parts.fft import ifft2
from mridc.collections.common.parts.utils import coil_combination
from mridc.collections.reconstruction.models.base import BaseMRIReconstructionModel
from mridc.collections.reconstruction.models.unet_base.unet_block import NormUnet
from mridc.collections.reconstruction.models.varnet.vn_block import VarNetBlock
from mridc.collections.reconstruction.parts.utils import center_crop_to_smallest
from mridc.core.classes.common import typecheck
__all__ = ["VarNet"]
[docs]class VarNet(BaseMRIReconstructionModel, ABC):
"""
Implementation of the End-to-end Variational Network (VN), as presented in Sriram, A. et al.
References
----------
..
Sriram, A. et al. (2020) ‘End-to-End Variational Networks for Accelerated MRI Reconstruction’. Available \
at: https://github.com/facebookresearch/fastMRI.
"""
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
# init superclass
super().__init__(cfg=cfg, trainer=trainer)
cfg_dict = OmegaConf.to_container(cfg, resolve=True)
self.no_dc = cfg_dict.get("no_dc")
self.fft_centered = cfg_dict.get("fft_centered")
self.fft_normalization = cfg_dict.get("fft_normalization")
self.spatial_dims = cfg_dict.get("spatial_dims")
self.coil_dim = cfg_dict.get("coil_dim")
self.num_cascades = cfg_dict.get("num_cascades")
# Cascades of VN blocks
self.cascades = torch.nn.ModuleList(
[
VarNetBlock(
NormUnet(
chans=cfg_dict.get("channels"),
num_pools=cfg_dict.get("pooling_layers"),
padding_size=cfg_dict.get("padding_size"),
normalize=cfg_dict.get("normalize"),
),
fft_centered=self.fft_centered,
fft_normalization=self.fft_normalization,
spatial_dims=self.spatial_dims,
coil_dim=self.coil_dim,
no_dc=self.no_dc,
)
for _ in range(self.num_cascades)
]
)
self.coil_combination_method = cfg_dict.get("coil_combination_method")
# initialize weights if not using pretrained vn
# TODO if not cfg_dict.get("pretrained", False)
self.train_loss_fn = SSIMLoss() if cfg_dict.get("train_loss_fn") == "ssim" else L1Loss()
self.eval_loss_fn = SSIMLoss() if cfg_dict.get("eval_loss_fn") == "ssim" else L1Loss()
self.dc_weight = torch.nn.Parameter(torch.ones(1))
self.accumulate_estimates = False
[docs] @typecheck()
def forward(
self,
y: torch.Tensor,
sensitivity_maps: torch.Tensor,
mask: torch.Tensor,
init_pred: torch.Tensor,
target: torch.Tensor,
) -> torch.Tensor:
"""
Forward pass of the network.
Parameters
----------
y: Subsampled k-space data.
torch.Tensor, shape [batch_size, n_coils, n_x, n_y, 2]
sensitivity_maps: Coil sensitivity maps.
torch.Tensor, shape [batch_size, n_coils, n_x, n_y, 2]
mask: Sampling mask.
torch.Tensor, shape [1, 1, n_x, n_y, 1]
init_pred: Initial prediction.
torch.Tensor, shape [batch_size, n_x, n_y, 2]
target: Target data to compute the loss.
torch.Tensor, shape [batch_size, n_x, n_y, 2]
Returns
-------
pred: list of torch.Tensor, shape [batch_size, n_x, n_y, 2], or torch.Tensor, shape [batch_size, n_x, n_y, 2]
If self.accumulate_loss is True, returns a list of all intermediate estimates.
If False, returns the final estimate.
"""
estimation = y.clone()
for cascade in self.cascades:
# Forward pass through the cascades
estimation = cascade(estimation, y, sensitivity_maps, mask)
estimation = ifft2(
estimation,
centered=self.fft_centered,
normalization=self.fft_normalization,
spatial_dims=self.spatial_dims,
)
estimation = coil_combination(
estimation, sensitivity_maps, method=self.coil_combination_method, dim=self.coil_dim
)
estimation = torch.view_as_complex(estimation)
_, estimation = center_crop_to_smallest(target, estimation)
return estimation