| --- |
| license: mit |
| library_name: glimpse-ct |
| tags: |
| - computed-tomography |
| - inverse-problems |
| - image-reconstruction |
| - implicit-neural-representation |
| - sparse-view-ct |
| pipeline_tag: image-to-image |
| datasets: |
| - AmirEhsan1995/lodopab-ct-glimpse |
| --- |
| |
| # GLIMPSE — Generalized Locality for Scalable and Robust CT |
|
|
| Coordinate-based CT reconstruction from sparse-view sinograms |
| ([paper](https://ieeexplore.ieee.org/abstract/document/11018464), |
| [arXiv](https://arxiv.org/abs/2401.00816), |
| [code](https://github.com/swing-research/Glimpse)), 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 |
|
|
| | | | |
| |---|---| |
| | Image size | 128 | |
| | Projection angles (views) | 50 | |
| | Noise (SNR, dB) | 45 | |
| | Forward operator | `odl` (circle=False) | |
| | Parameters | ~1.3 M | |
|
|
| ## Results (LoDoPaB-CT, 50 views, calibrated) |
|
|
| GLIMPSE substantially outperforms classical filtered back-projection (FBP), both |
| in-distribution and out-of-distribution: |
|
|
| | Set | FBP PSNR / SSIM | GLIMPSE PSNR / SSIM | |
| |---|---|---| |
| | In-distribution (LoDoPaB-CT test) | 30.8 dB / 0.79 | **38.0 dB / 0.93** | |
| | Out-of-distribution (brain CT) | 26.1 dB / 0.51 | **31.6 dB / 0.88** | |
|
|
| ## Usage |
|
|
| ```python |
| 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](https://github.com/swing-research/Glimpse/blob/main/notebooks/inference_demo.ipynb) |
| for an end-to-end example (data download, FBP baseline, PSNR/SSIM, figures). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
