GLIMPSE — Generalized Locality for Scalable and Robust CT

Coordinate-based CT reconstruction from sparse-view sinograms (paper, arXiv, code), published in IEEE Transactions on Medical Imaging. Instead of reconstructing a whole image at once, GLIMPSE predicts one pixel at a time from only the sinogram data local to that pixel's coordinate. This locality makes it resolution-agnostic and gives strong out-of-distribution generalization — e.g. train on natural images / faces and reconstruct medical brain scans without retraining.

This checkpoint

Results (LoDoPaB-CT, 50 views, calibrated)

GLIMPSE substantially outperforms classical filtered back-projection (FBP), both in-distribution and out-of-distribution:

Usage

import numpy as np, torch
from glimpse import GlimpseModel, Config
from glimpse.operators import build_operator
from glimpse.reconstruct import make_coordinate_grid, reconstruct_image

device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = GlimpseModel.from_pretrained("AmirEhsan1995/Glimpse").eval().to(device)

# Build the matching ODL parallel-beam operator (50 views over [0, 180) deg).
cfg = Config.from_yaml('configs/lodopab.yaml')        # from the GitHub repo
_, init_angles = cfg.resolve_angles()
operator = build_operator(cfg, np.deg2rad(init_angles))

volume = torch.as_tensor(my_image[None], dtype=torch.float32, device=device)  # (1, H, W)
sino = operator.project(volume)                       # sparse-view sinogram
coords = make_coordinate_grid(cfg.image_size).unsqueeze(0).to(device)
recon = reconstruct_image(sino, coords, 1, model, chunk_size=1024)
recon = recon.reshape(cfg.image_size, cfg.image_size)

See the demo notebook for an end-to-end example (data download, FBP baseline, PSNR/SSIM, figures).

Citation

@article{khorashadizadeh2025glimpse,
  title   = {GLIMPSE: Generalized Locality for Scalable and Robust CT},
  author  = {Khorashadizadeh, AmirEhsan and Debarnot, Valentin and Liu, Tianlin and Dokmani{\'c}, Ivan},
  journal = {IEEE Transactions on Medical Imaging},
  year    = {2025},
  doi     = {10.1109/TMI.2025.3568017}
}