This repository provides PyTorch bindings for PYRO-NN, a collection of back-propagatable projectors for CT reconstruction.
Feel free to cite our publication:
@article{PYRONN2019,
author = {Syben, Christopher and Michen, Markus and Stimpel, Bernhard and Seitz, Stephan and Ploner, Stefan and Maier, Andreas K.},
title = {Technical Note: PYRO-NN: Python reconstruction operators in neural networks},
year = {2019},
journal = {Medical Physics},
}From PyPI:
pip install pyronn-torchFrom this repository:
git clone --recurse-submodules --recursive https://github.com/theHamsta/pyronn-torch.git
cd pyronn-torch
pip install torch
pip install -e .You can build a binary wheel using
python setup.py bdist_wheelImportant
If you're using an older CUDA version you might get an error about '-allow-unsupported-compiler' not being a
valid compiler option. In that case remove that compiler option from this project's setup.py.
import pyronn_torch
import numpy as np
projector = pyronn_torch.ConeBeamProjector(
(128, 128, 128), # volume shape
(2.0, 2.0, 2.0), # volume spacing in mm
(-127.5, -127.5, -127.5), # volume origin in mm
(2, 480, 620), # projection_shape (number of projections, height, width)
[1.0, 1.0], # projection_spacing in mm
(0, 0), # projection_origin
np.array([[[-3.10e+2, -1.20e+03, 0.00e+00, 1.86e+5],
[-2.40e+2, 0.00e+00, 1.20e+03, 1.44e+5],
[-1.00e+00, 0.00e+00, 0.00e+00, 6.00e+2]],
[[-2.89009888e+2, -1.20522754e+3, -1.02473585e-13,
1.86000000e+5],
[-2.39963440e+2, -4.18857765e+0, 1.20000000e+3,
1.44000000e+5],
[-9.99847710e-01, -1.74524058e-2, 0.00000000e+0,
6.00000000e+2]]]) # two projection matrices in shape (n, 3, 4)
# optionally: source_isocenter_distance=1, source_detector_distance=1 for a scalar weighting the projections
)
projection = projector.new_projection_tensor(requires_grad=True)
projection = projection + 1.
result = projector.project_backward(projection, use_texture=True)
assert projection.requires_grad
assert result.requires_grad
loss = result.mean()
loss.backward()Or easier with PyCONRAD (pip install pyconrad)
projector = pyronn_torch.ConeBeamProjector.from_conrad_config()The configuration can then be done using CONRAD
(startable using conrad from command line)