DRR

Module for computing digitally reconstructed radiographs

DRR

DRR is a PyTorch module that compues differentiable digitally reconstructed radiographs. The viewing angle for the DRR (known generally in computer graphics as the camera pose) is parameterized by the following parameters:

SDD : source-to-detector distance (i.e., the focal length of the C-arm)
\(\mathbf R \in \mathrm{SO}(3)\) : a rotation
\(\mathbf t \in \mathbb R^3\) : a translation

Tip

DiffDRR can take a rotation parameterized in any of the following forms to move the detector plane:

axis_angle
euler_angles (note: also need to specify the convention for the Euler angles)
matrix
quaternion
quaternion_adjugate (Hanson and Hanson, 2022)
rotation_6d (Zhou et al., 2019)
rotation_10d (Peretroukhin et al., 2021)`
se3_log_map

If using Euler angles, the parameters are

alpha : Azimuthal angle
beta : Polar angle
gamma : Plane rotation angle
bx : X-dir translation
by : Y-dir translation
bz : Z-dir translation
convention : Order of angles (e.g., ZYX)

(bx, by, bz) are translational parameters and (alpha, beta, gamma) are rotational parameters.

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DRR

 DRR (subject:torchio.data.subject.Subject, sdd:float, height:int,
      delx:float, width:int|None=None, dely:float|None=None, x0:float=0.0,
      y0:float=0.0, p_subsample:float|None=None, reshape:bool=True,
      reverse_x_axis:bool=True, patch_size:int|None=None,
      renderer:str='siddon', persistent:bool=True, **renderer_kwargs)

PyTorch module that computes differentiable digitally reconstructed radiographs.

	Type	Default	Details
subject	Subject		TorchIO wrapper for the CT volume
sdd	float		Source-to-detector distance (i.e., the C-arm’s focal length)
height	int		Height of the rendered DRR
delx	float		X-axis pixel size
width	int \| None	None	Width of the rendered DRR (default to `height`)
dely	float \| None	None	Y-axis pixel size (if not provided, set to `delx`)
x0	float	0.0	Principal point X-offset
y0	float	0.0	Principal point Y-offset
p_subsample	float \| None	None	Proportion of pixels to randomly subsample
reshape	bool	True	Return DRR with shape (b, 1, h, w)
reverse_x_axis	bool	True	If True, obey radiologic convention (e.g., heart on right)
patch_size	int \| None	None	Render patches of the DRR in series
renderer	str	siddon	Rendering backend, either “siddon” or “trilinear”
persistent	bool	True	Set persistent value in `torch.nn.Module.register_buffer`
renderer_kwargs

The forward pass of the DRR module generated DRRs from the input CT volume. The pose parameters (i.e., viewing angles) from which the DRRs are generated are passed to the forward call.

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DRR.render

 DRR.render (density:<built-inmethodtensoroftypeobjectat0x7f8687e91080>,
             source:<built-inmethodtensoroftypeobjectat0x7f8687e91080>,
             target:<built-inmethodtensoroftypeobjectat0x7f8687e91080>,
             mask_to_channels:bool=False, **kwargs)

	Type	Default	Details
density	tensor		Volume from which to render DRRs
source	tensor		World coordinates of X-ray source
target	tensor		World coordinates of X-ray target
mask_to_channels	bool	False	If True, structures from the CT mask are rendered in separate channels
kwargs

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DRR.forward

 DRR.forward (*args, parameterization:str=None, convention:str=None,
              calibration:diffdrr.pose.RigidTransform=None,
              mask_to_channels:bool=False, **kwargs)

Generate DRR with rotational and translational parameters.

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DRR.rescale_detector_

 DRR.rescale_detector_ (scale:float)

Rescale the detector plane (inplace).

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DRR.perspective_projection

 DRR.perspective_projection (pose:diffdrr.pose.RigidTransform,
                             pts:torch.Tensor)

Project points in world coordinates (3D) onto the pixel plane (2D).

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DRR.inverse_projection

 DRR.inverse_projection (pose:diffdrr.pose.RigidTransform,
                         pts:torch.Tensor)

Backproject points in pixel plane (2D) onto the image plane in world coordinates (3D).