mridc.collections.reconstruction.models.multidomain package

Submodules

mridc.collections.reconstruction.models.multidomain.multidomain module

class mridc.collections.reconstruction.models.multidomain.multidomain.MultiDomainConv2d(fft_centered: bool = True, fft_normalization: str = 'ortho', spatial_dims: Optional[Sequence[int]] = None, coil_dim: int = 1, in_channels: int = 4, out_channels: int = 4, **kwargs)

Bases: Module

Multi-domain convolution layer.

forward(image)

Forward method for the MultiDomainConv2d class.

training: bool

class mridc.collections.reconstruction.models.multidomain.multidomain.MultiDomainConvBlock(fft_centered: bool = True, fft_normalization: str = 'ortho', spatial_dims: Optional[Sequence[int]] = None, coil_dim: int = 1, in_channels: int = 4, out_channels: int = 4, dropout_probability: float = 0.0)

Bases: Module

A multi-domain convolutional block that consists of two multi-domain convolution layers each followed by instance normalization, LeakyReLU activation and dropout.

forward(_input: Tensor)

Forward method for the MultiDomainConvBlock class.

training: bool

class mridc.collections.reconstruction.models.multidomain.multidomain.MultiDomainConvTranspose2d(fft_centered: bool = True, fft_normalization: str = 'ortho', spatial_dims: Optional[Sequence[int]] = None, coil_dim: int = 1, in_channels: int = 4, out_channels: int = 4, **kwargs)

Bases: Module

Multi-Domain convolutional transpose layer.

forward(image)

Forward method for the MultiDomainConvTranspose2d class.

training: bool

class mridc.collections.reconstruction.models.multidomain.multidomain.MultiDomainUnet2d(in_channels: int, out_channels: int, num_filters: int, num_pool_layers: int, dropout_probability: float, fft_centered: bool = True, fft_normalization: str = 'ortho', spatial_dims: Optional[Tuple[int, int]] = None, coil_dim: int = 1)

Bases: Module

Unet modification to be used with Multi-domain network as in AIRS Medical submission to the Fast MRI 2020 challenge.

forward(input_data: Tensor)

Forward pass of the u-net.

training: bool

class mridc.collections.reconstruction.models.multidomain.multidomain.StandardizationLayer(coil_dim=1, channel_dim=-1)

Bases: Module

Multi-channel data standardization method. Inspired by AIRS model submission to the Fast MRI 2020 challenge. Given individual coil images \(\{x_i\}_{i=1}^{N_c}\) and sensitivity coil maps \(\{S_i\}_{i=1}^{N_c}\) it returns

\[[(x_{sense}, {x_{res}}_1), ..., (x_{sense}, {x_{res}}_{N_c})]\]

where

\({x_{res}}_i = xi - S_i X x_{sense}\) and

\(x_{sense} = \sum_{i=1}^{N_c} {S_i}^{*} X x_i\).

forward(coil_images: Tensor, sensitivity_map: Tensor) → Tensor

Forward pass.

training: bool

class mridc.collections.reconstruction.models.multidomain.multidomain.TransposeMultiDomainConvBlock(fft_centered: bool = True, fft_normalization: str = 'ortho', spatial_dims: Optional[Sequence[int]] = None, coil_dim: int = 1, in_channels: int = 4, out_channels: int = 4)

Bases: Module

A Transpose Convolutional Block that consists of one convolution transpose layers followed by instance normalization and LeakyReLU activation.

forward(input_data: Tensor)

Forward method for the TransposeMultiDomainConvBlock class.

training: bool

Module contents