torchtomo 0.1.0

Differentiable CT Reconstruction in Pure PyTorch

TorchTomo

Differentiable CT reconstruction primitives in pure PyTorch.

TorchTomo provides forward projection, adjoint backprojection, and filtered backprojection for parallel-beam and fan-beam geometries, with support for CPU, CUDA, and Apple Silicon (MPS).

Features

• Pure PyTorch implementation with no custom CUDA build step
• Autograd-friendly operators for learned reconstruction pipelines
• Parallel-beam and fan-beam (flat detector) projectors
• Built-in FBP filters: ramp, shepp-logan, cosine, hamming, hann, none
• Built-in phantom generators for quick experiments

Installation

pip install torchtomo

Quick Start (Parallel Beam)

import torch
from torchtomo import ParallelBeam, shepp_logan

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

phantom = shepp_logan(size=256, device=device)  # [1, 1, 256, 256]
projector = ParallelBeam(img_size=256, n_angles=180, n_det=256).to(device)

sinogram = projector.forward(phantom)                 # [1, 1, 180, 256]
recon = projector.fbp(sinogram, filter_name="ramp")   # [1, 1, 256, 256]

Fan-Beam Example

from torchtomo import FanBeam, shepp_logan

phantom = shepp_logan(size=256)
projector = FanBeam(
    img_size=256,
    n_angles=360,
    n_det=400,
    src_dist=500.0,
    det_dist=500.0,
)

sinogram = projector.forward(phantom)
recon = projector.fbp(sinogram, filter_name="hann")

Differentiable Optimization Example

import torch
import torch.nn.functional as F
from torchtomo import ParallelBeam

projector = ParallelBeam(img_size=256, n_angles=180)
x = torch.zeros(1, 1, 256, 256, requires_grad=True)
y = torch.randn(1, 1, 180, 256)

loss = F.mse_loss(projector.forward(x), y)
loss.backward()  # gradients flow through projection operators

API Snapshot

• ParallelBeam(...)
• FanBeam(...)
• projector.forward(image)
• projector.backward(sinogram)
• projector.fbp(sinogram, filter_name="ramp")
• apply_filter(sinogram, filter_name=...)
• shepp_logan(size=..., device=...)
• circle_phantom(size=..., n_circles=..., device=...)
• torchtomo.phantom.forbild(size=..., device=...)

Tensor Shapes

• Image: [B, 1, H, W]
• Sinogram: [B, 1, n_angles, n_det]

Development

git clone https://github.com/itu-biai/torchtomo.git
cd torchtomo
pip install -e ".[dev]"

make test
make lint
make build

CI/CD

• .github/workflows/test.yml: Python test matrix on push and pull_request
• .github/workflows/publish.yml: release-triggered test matrix and PyPI publish step

License

MIT