mridc.collections.reconstruction.models.variablesplittingnet package

Submodules

mridc.collections.reconstruction.models.variablesplittingnet.vsnet_block module

class mridc.collections.reconstruction.models.variablesplittingnet.vsnet_block.DataConsistencyLayer[source]

Bases: Module

Data consistency layer for the VSNet. This layer is used to ensure that the output of the VSNet is the same as the input.

forward(pred_kspace, ref_kspace, mask)[source]

Forward pass of the data consistency layer.

training: bool
class mridc.collections.reconstruction.models.variablesplittingnet.vsnet_block.VSNetBlock(denoiser_block: ModuleList, data_consistency_block: ModuleList, weighted_average_block: ModuleList, num_cascades: int = 8, fft_centered: bool = True, fft_normalization: str = 'ortho', spatial_dims: Optional[Tuple[int, int]] = None, coil_dim: int = 1)[source]

Bases: Module

Model block for the Variable-Splitting Network inspired by 1.

References

1

Duan, J. et al. (2019) ‘Vs-net: Variable splitting network for accelerated parallel MRI reconstruction’, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11767 LNCS, pp. 713–722. doi: 10.1007/978-3-030-32251-9_78.

forward(kspace: Tensor, sens_maps: Tensor, mask: Tensor) → List[Union[Tensor, Any]][source]
Parameters

  • kspace (Reference k-space data.) –

  • sens_maps (Coil sensitivity maps.) –

  • mask (Mask to apply to the data.) –

Return type

Reconstructed image.

sens_expand(x: Tensor, sens_maps: Tensor) → Tensor[source]

Expand the sensitivity maps to the same size as the input.

Parameters

  • x (Input data.) –

  • sens_maps (Coil Sensitivity maps.) –

Return type

SENSE reconstruction expanded to the same size as the input sens_maps.

sens_reduce(x: Tensor, sens_maps: Tensor) → Tensor[source]

Reduce the sensitivity maps.

Parameters

  • x (Input data.) –

  • sens_maps (Coil Sensitivity maps.) –

Return type

SENSE coil-combined reconstruction.

training: bool
class mridc.collections.reconstruction.models.variablesplittingnet.vsnet_block.WeightedAverageTerm[source]

Bases: Module

Weighted average term for the VSNet.

forward(x, Sx)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

Module contents