3D geometry in DiffDRR

Understanding X-ray detector geometry

This is a tutorial on the 3D geometry used in DiffDRR. It includes:

3D visualization with PyVista

Visualizing the 3D geometry of the X-ray detector in DiffDRR can be a helpful sanity check and is useful for debugging. We enable visualization of the DiffDRR setup using PyVista. The dependencies are pyvista, trame, and vtk.

The 3D visualization functions in DiffDRR perform the following steps:

  • Extract a mesh from your CT volume
  • Plot a pyramid frustum to visualize the camera pose
  • Plot the detector plane with the DRR embedded as a texture
  • Draw the principal ray from the X-ray source to the detector plane

We currently support the following backends for extracting meshes from CT scans:

As of DiffDRR v0.4.0, we also support the rendering of 3D labelmaps (e.g., segmentations of CT scans with TotalSegmentator).

Tip

To use surface_nets to extract a mesh, ensure you have installed pyvista>=0.43 and vtk>=9.3. Otherwise, you can use marching_cubes, which is slower and produces meshes with holes.

Code
import matplotlib.pyplot as plt
import pyvista
import torch

from diffdrr.data import load_example_ct
from diffdrr.drr import DRR
from diffdrr.pose import convert
from diffdrr.visualization import drr_to_mesh, img_to_mesh, labelmap_to_mesh, plot_drr

pyvista.start_xvfb()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Read in the CT volume
subject = load_example_ct()

# Make a mesh from the CT volume
ct = drr_to_mesh(subject, "surface_nets", threshold=225, verbose=True)
Performing Labeled Surface Extraction: 100%|█████████████████████████████████████████████████████████████[00:01<00:00]
Finding and Labeling Connected Regions.: 100%|███████████████████████████████████████████████████████████[00:01<00:00]
Smoothing Mesh using Taubin Smoothing: 100%|█████████████████████████████████████████████████████████████[00:05<00:00]
Filling Holes: 100%|█████████████████████████████████████████████████████████████████████████████████████[00:00<00:00]
Cleaning: 100%|██████████████████████████████████████████████████████████████████████████████████████████[00:00<00:00]
# Initialize the DRR module for generating synthetic X-rays
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
drr = DRR(subject, sdd=1020.0, height=200, delx=2.0).to(device)

# Make a pose
rot = torch.tensor([[45.0, 30.0, 0.0]], device=device) / 180 * torch.pi
xyz = torch.tensor([[0.0, 800.0, 0.0]], device=device)
pose = convert(rot, xyz, parameterization="euler_angles", convention="ZXY")
plot_drr(drr(pose))
plt.show()

# Make a mesh from the camera and detector plane
camera, detector, texture, principal_ray = img_to_mesh(drr, pose)

# Make the plot
plotter = pyvista.Plotter()
plotter.add_mesh(ct)
plotter.add_mesh(camera, show_edges=True, line_width=1.5)
plotter.add_mesh(principal_ray, color="lime", line_width=3)
plotter.add_mesh(detector, texture=texture)

# Render the plot
plotter.add_axes()
plotter.add_bounding_box()
plotter.show_bounds(grid="front", location="outer", all_edges=True)

# plotter.show()  # If running Jupyter locally
# plotter.show(jupyter_backend="server")  # If running Jupyter remotely
plotter.export_html("render.html")
from IPython.display import IFrame

IFrame("render.html", height=500, width=749)

Rendering labelmaps

The SurfaceNets algorithm was actually originally designed for the visualization of 3D labelmaps. Running it on segmentation masks produced by TotalSegmentator produces detailed renderings.

from matplotlib.colors import ListedColormap

mask = labelmap_to_mesh(drr.subject)
cmap = ListedColormap(
    [
        "#66c2a5",
        "#fc8d62",
        "#8da0cb",
        "#e78ac3",
        "#a6d854",
        "#ffd92f",
        "#e5c494",
        "#b3b3b3",
    ]
    * 15
)

plotter = pyvista.Plotter()
plotter.add_mesh(mask, cmap=cmap)
plotter.add_mesh(camera, show_edges=True, line_width=1.5)
plotter.add_mesh(principal_ray, color="lime", line_width=3)
plotter.add_mesh(detector, texture=texture)
plotter.add_axes()
plotter.add_bounding_box()
plotter.remove_scalar_bar()
plotter.export_html("mask.html")
Performing Labeled Surface Extraction: 100%|█████████████████████████████████████████████████████████████[00:02<00:00]
Smoothing Mesh using Taubin Smoothing: 100%|█████████████████████████████████████████████████████████████[00:07<00:00]
Cleaning: 100%|██████████████████████████████████████████████████████████████████████████████████████████[00:00<00:00]
IFrame("mask.html", height=500, width=749)

Projective geometry

X-ray imaging systems can be accurately modelled as pinhole cameras. What follows is a brief description of the projective geometry underlying pinhole cameras, as it applies to DiffDRR. A more comprehensive overview can be found in Hartley and Zisserman (Chapter 5) or numerous online resources.

Intrinsic parameters

In DiffDRR, the intrinsic parameters are

  • sdd : the source to detector distance (i.e., the C-arm’s focal length)
  • delx : the x-direction pixel spacing (in mm)
  • dely : the y-direction pixel spacing (in mm)
  • x0 : the principal point offset in the x-direction
  • y0 : the principal point offset in the y-direction

These direclty form the intrinsic matrix (with physical units) of a pinhole camera. For most imaging systems, these parameters are directly found in the DICOM.

Extrinsic parameters

The extrinsic parameters comprise an affine transform, written as \(\mathbf T \in \mathbf{SE}(3)\), which is an element of a particular manifold of \(4 \times 4\) matrices. There are many ways to parameterize affine transforms. For particulars on how this is implemented in DiffDRR, see the module diffdrr.pose.

The simplest way of parameterizing an affine transform is with a translation and a rotation written in Euler angles. In fact, this is how camera poses are often stored in DICOM.

Relation to C-arms

For an example, consider the pose rendered above:

rot = torch.tensor([[45.0, 30.0, 0.0]], device=device) / 180 * torch.pi
xyz = torch.tensor([[0.0, 800.0, 0.0]], device=device)
pose = convert(rot, xyz, parameterization="euler_angles", convention="ZXY")

This corresponds to the following sequence of transforms:

  • A translation of +800 along the y-axis
  • A rotation by 45 degrees about the z-axis
  • A rotation by 30 degress about the x-axis

which produces the oblique view visualized above.

What if the C-arm is behind the patient?

Many medical disciplies have C-arms where the source is positioned behind the patient (e.g., X-ray angiography). In these cases, the CT can be loaded with the PA orientation:

subject = load_example_ct(orientation="PA")

This will produce the desired behavior. The only difference is that the y parameter in rot needs to be negated.

On representations of rotations

There are many ways to represent 3D rotations, and Euler angles are but one option. In general, Euler angles are the most human-understandable parameterization. However, they’re not the best for optimization problems. For solving problems like 2D/3D registration, higher-dimensional representations have emperically better performance.