trw.transforms.resample

Module Contents

Functions

mm_list(matrices: List[torch.Tensor])

Multiply all matrices

affine_grid_fixed_to_moving(geometry_moving: trw.transforms.spatial_info.SpatialInfo, geometry_fixed: trw.transforms.spatial_info.SpatialInfo, tfm: torch.Tensor, align_corners: bool = False) → torch.Tensor

Calculate a grid that maps a fixed geometry to a transformed moving geometry.

resample_spatial_info(geometry_moving: trw.transforms.spatial_info.SpatialInfo, moving_volume: trw.basic_typing.TorchTensorNCX, geometry_fixed: trw.transforms.spatial_info.SpatialInfo, tfm: torch.Tensor, interpolation: typing_extensions.Literal[linear, nearest] = 'linear', padding_mode: typing_extensions.Literal[zeros, border, reflection] = 'zeros', align_corners: bool = False) → trw.basic_typing.TorchTensorNCX

Apply an affine transformation to a given (moving) volume into a given geometry (fixed)

resample_np_volume_3d(np_volume: trw.basic_typing.NumpyTensorX, np_volume_spacing: trw.basic_typing.Length, np_volume_origin: trw.basic_typing.Length, min_bb_mm: trw.basic_typing.Length, max_bb_mm: trw.basic_typing.Length, resampled_spacing: trw.basic_typing.Length, mode: typing_extensions.Literal[reflect, constant, nearest, mirror, wrap] = 'constant', constant_value: trw.basic_typing.Numeric = 0.0, order=1) → trw.basic_typing.NumpyTensorX

Resample a portion of a 3D volume (z, y, x) to a specified spacing/bounding box.

resample_3d(volume: trw.basic_typing.TensorX, np_volume_spacing: trw.basic_typing.Length, np_volume_origin: trw.basic_typing.Length, min_bb_mm: trw.basic_typing.Length, max_bb_mm: trw.basic_typing.Length, resampled_spacing: trw.basic_typing.Length, interpolation_mode: typing_extensions.Literal[linear, nearest] = 'linear', padding_mode: typing_extensions.Literal[zeros, border, reflection] = 'zeros', align_corners=False) → trw.basic_typing.TensorX

trw.transforms.resample.mm_list(matrices: List[torch.Tensor])

Multiply all matrices

trw.transforms.resample.affine_grid_fixed_to_moving(geometry_moving: trw.transforms.spatial_info.SpatialInfo, geometry_fixed: trw.transforms.spatial_info.SpatialInfo, tfm: torch.Tensor, align_corners: bool = False) → torch.Tensor

Calculate a grid that maps a fixed geometry to a transformed moving geometry.

This can be used to resampled a volume to a different geometry / transformation.

Parameters

  • geometry_moving – the moving geometry. This geometry will have an affine transformation tfm applied (e.g., translation, scaling)

  • geometry_fixed – the fixed geometry

  • tfm – a linear transformation that will move moving_volume

  • align_corners – should be false

Returns

a N x D x C x H x W x dim grid

trw.transforms.resample.resample_spatial_info(geometry_moving: trw.transforms.spatial_info.SpatialInfo, moving_volume: trw.basic_typing.TorchTensorNCX, geometry_fixed: trw.transforms.spatial_info.SpatialInfo, tfm: torch.Tensor, interpolation: typing_extensions.Literal[linear, nearest] = 'linear', padding_mode: typing_extensions.Literal[zeros, border, reflection] = 'zeros', align_corners: bool = False) → trw.basic_typing.TorchTensorNCX

Apply an affine transformation to a given (moving) volume into a given geometry (fixed)

Parameters

  • geometry_moving – Defines the geometric space of the moving volume

  • moving_volume – the moving volume (2D or 3D)

  • geometry_fixed – define the geometric space to be resampled

  • tfm – an (dim + 1) x (dim + 1) affine transformation matrix that moves the moving volume

  • interpolation – how to interpolate the moving volume

  • padding_mode – defines how to handle missing (moving) data

  • align_corners – specifies how to align the voxel grids

Returns

a volume with geometric space geometry_fixed. The content is the moving_volume moved by tfm

Notes

the gradient will be propagated through the transform

trw.transforms.resample.resample_np_volume_3d(np_volume: trw.basic_typing.NumpyTensorX, np_volume_spacing: trw.basic_typing.Length, np_volume_origin: trw.basic_typing.Length, min_bb_mm: trw.basic_typing.Length, max_bb_mm: trw.basic_typing.Length, resampled_spacing: trw.basic_typing.Length, mode: typing_extensions.Literal[reflect, constant, nearest, mirror, wrap] = 'constant', constant_value: trw.basic_typing.Numeric = 0.0, order=1) → trw.basic_typing.NumpyTensorX

Resample a portion of a 3D volume (z, y, x) to a specified spacing/bounding box.

Parameters

  • np_volume – a 3D volume

  • np_volume_spacing – the spacing [sz, sy, sx] of the input volume

  • np_volume_origin – the origin [z, y, x] of the input volume

  • min_bb_mm – the min position [z, y, x] of the input volume to be resampled

  • max_bb_mm – the max position [z, y, x] of the input volume to be resampled

  • resampled_spacing – the spacing of the resampled volume

  • mode – specifies how to handle the boundary. See scipy.ndimage.affine_transform()

  • constant_value – if mode == constant, use constant_value as background value

  • order – interpolation order [0..5]

Returns

resampled volume

Deprecated since version 0.0.2: Use resample_3d instead! This is just for comparison

trw.transforms.resample.resample_3d(volume: trw.basic_typing.TensorX, np_volume_spacing: trw.basic_typing.Length, np_volume_origin: trw.basic_typing.Length, min_bb_mm: trw.basic_typing.Length, max_bb_mm: trw.basic_typing.Length, resampled_spacing: trw.basic_typing.Length, interpolation_mode: typing_extensions.Literal[linear, nearest] = 'linear', padding_mode: typing_extensions.Literal[zeros, border, reflection] = 'zeros', align_corners=False) → trw.basic_typing.TensorX