BaseMaskGenerator
- class deepinv.physics.generator.BaseMaskGenerator(img_size: Tuple, acceleration: int = 4, center_fraction: float | None = None, rng: Generator | None = None, device='cpu', *args, **kwargs)[source]
Bases:
PhysicsGenerator
Base generator for MRI acceleration masks.
Generate a mask of vertical lines for MRI acceleration with fixed sampling in low frequencies (center of k-space) and undersampling in the high frequencies.
The type of undersampling is determined by the child class. The mask is repeated across channels and randomly varies across batch dimension.
- Parameters:
img_size (Tuple) – image size, either (H, W) or (C, H, W) or (C, T, H, W), where optional C is channels, and optional T is number of time-steps
acceleration (int) – acceleration factor, defaults to 4
center_fraction (float) – fraction of lines to sample in low frequencies (center of k-space). If 0, there is no fixed low-freq sampling. Defaults to None.
rng (torch.Generator) – torch random generator. Defaults to None.
- sample_mask(mask: Tensor) Tensor [source]
Given empty mask, sample lines according to child class sampling strategy.
This must be implemented in child classes. Time dimension is specified but can be ignored if needed.
- Parameters:
mask (torch.Tensor) – empty mask of shape (B, C, T, H, W)
- Return torch.Tensor:
sampled mask of shape (B, C, T, H, W)
Examples using BaseMaskGenerator
:
A tour of forward sensing operators
Self-supervised MRI reconstruction with Artifact2Artifact