Getting Started

This document should get you up and running with FlashDRR!

What is FlashDRR?

FlashDRR is a small, focused library for differentiable DRR (Digitally Reconstructed Radiograph) raycasting and volume rendering in PyTorch. It turns CT volumes into synthetic X-ray projections while keeping gradients flowing through both the volume and the camera parameters, which makes it a natural fit for deep-learning pipelines in 2D/3D registration, pose estimation, and synthetic-image generation.

The pre-0.4.x “data loading framework” functionality (TorchDataset / TorchQueueDataset, HDF5 & DICOM converters, transforms) has been trimmed. The current focus is the renderer and its camera geometry.

Installation

Latest PyPI release

pip install flashdrr

Latest master

pip install git+https://github.com/torchvtk/torchvtk.git@master#egg=flashdrr

Developer installation

This will install your local changes to flashdrr directly.

git clone https://github.com/torchvtk/torchvtk.git
cd torchvtk
pip install -e .

Features

Differentiable DRR raycasting

The core of the library is flashdrr.rendering.VolumeRaycaster. It integrates attenuation along each ray (Beer–Lambert law by default) and is fully differentiable with respect to both the volume and the view matrix.

Check out Rendering for a detailed guide and end-to-end example.

Fast Triton-fused rendering

flashdrr.rendering.FusedVolumeRenderer is a Triton-fused CUDA kernel that the VolumeRaycaster uses when triton=True. For high-resolution or many view renders it provides an order-of-magnitude speedup over the pure-PyTorch loop.

C-arm / camera geometry

Realistic X-ray gantry poses are easy to sample with carm_to_camera_params and get_random_carm_views, and VTK-compatible camera matrices can be built with get_vtk_view_mat.

Volume utilities

flashdrr.utils provides small helpers for working with volumetric tensors, such as make_nd, normalize_hounsfield and normalize_voxel_scale.

A minimal end-to-end example

The following renders a DRR from a random attenuation volume:

import torch
import flashdrr.rendering as R
from flashdrr.rendering import carm_to_camera_params, get_vtk_view_mat

# (B, C, D, H, W) attenuation volume; ras2ijk: (4, 4) RAS -> IJK affine
vol   = torch.rand(1, 1, 128, 128, 128)
ras2ijk = torch.eye(4)

raycaster = R.VolumeRaycaster(ray_samples=256, resolution=(512, 512))

center   = torch.tensor([64., 64., 64.])
pos, focal, up = carm_to_camera_params(
    sid=1000.0, ap_angle=0.0, lat_angle=-30.0,
    center_ras=center, table_si=0.0)
view_mat = get_vtk_view_mat(pos, focal, up).unsqueeze(0)

drr = raycaster(vol, view_mat=view_mat, ras2ijk=ras2ijk)
# drr: (1, 1, 512, 512) projection

Contributing

The project is small and focused. We are open for all suggestions right on our GitHub. Just throw us an issue ;)