visualization

Visualize and animate DRRs in 2D and 3D

2D Visualization

Uses matplotlib and imageio to plot DRRs and animate optimization over DRRs.


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plot_drr

 plot_drr (img:torch.Tensor, title:str|None=None, ticks:bool|None=True,
           axs:matplotlib.axes._axes.Axes|None=None, cmap:str='gray',
           **imshow_kwargs)

Plot an image generated by a DRR module.


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plot_mask

 plot_mask (img:torch.Tensor, axs:matplotlib.axes._axes.Axes,
            colors=['rgb(102,194,165)', 'rgb(252,141,98)',
            'rgb(141,160,203)', 'rgb(231,138,195)', 'rgb(166,216,84)',
            'rgb(255,217,47)', 'rgb(229,196,148)'], alpha=0.625,
            return_masks=False)

Plot a 2D rendered segmentation mask. Meant to be called after plot_drr.


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animate

 animate (out:str|pathlib.Path, df:pandas.core.frame.DataFrame,
          drr:diffdrr.drr.DRR, parameterization:str, convention:str=None,
          ground_truth:torch.Tensor|None=None, verbose:bool=True,
          dtype=torch.float32, device='cpu', **kwargs)

Animate the optimization of a DRR.

Type Default Details
out str | pathlib.Path Savepath
df pandas.DataFrame
drr DRR
parameterization str
convention str None
ground_truth torch.Tensor | None None
verbose bool True
dtype dtype torch.float32
device str cpu
kwargs

df is a pandas.DataFrame with columns ["alpha", "beta", "gamma", "bx", "by", "bz"]. Each row in df is an iteration of optimization with the updated values for that timestep.

3D Visualization

Uses pyvista and trame to interactively visualize DRR geometry in 3D.


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drr_to_mesh

 drr_to_mesh (subject:torchio.data.subject.Subject, method:str,
              threshold:float=300, verbose:bool=True)

*Convert the CT in a DRR object into a mesh.

If using method=="surface_nets", ensure you have pyvista>=0.43 and vtk>=9.3 installed.

The mesh processing steps are:

  1. Keep only largest connected components
  2. Smooth
  3. Decimate (if method=="marching_cubes")
  4. Fill any holes
  5. Clean (remove any redundant vertices/edges)*
Type Default Details
subject Subject torchio.Subject with a volume attribute
method str Either surface_nets or marching_cubes
threshold float 300 Min value for marching cubes (Hounsfield units)
verbose bool True Display progress bars for mesh processing steps

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labelmap_to_mesh

 labelmap_to_mesh (subject:torchio.data.subject.Subject,
                   verbose:bool=True)
Type Default Details
subject Subject torchio.Subject with a mask attribute
verbose bool True Display progress bars for mesh processing steps

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img_to_mesh

 img_to_mesh (drr:diffdrr.drr.DRR, pose:diffdrr.pose.RigidTransform,
              calibration:diffdrr.pose.RigidTransform=None, **kwargs)

For a given pose (not batched), turn the camera and detector into a mesh. Additionally, render the DRR for the pose. Convert into a texture that can be applied to the detector mesh.