FORCE-Interior checkpoints

Pretrained weights for FORCE-Interior: Measurement-Consistent Adaptation of a Poisson-Flow Generative Prior for Interior CT, together with the learned baselines it is compared against.

Code: https://github.com/Peteroos/FORCE-Interior

Files

Path	Size	Model	Used by
checkpoints/model_028.pth	2.3 GB	PFGM++ prior of FORCE-Interior	run_force.py
checkpoints/roi_ct_cnn/net-epoch-200.pth	62 MB	ROI-CT-CNN U-Net	run_roicnn.py
checkpoints/dds_pretrained/lodochallenge_pixel_diffusion/	455 MB	DDS pixel-diffusion prior (third-party, mirrored)	run_dds.py
checkpoints/drunet/drunet_mayo.pth	131 MB	DRUNet denoiser for the PnP-ADMM / PnP-HQS baselines	PnP baselines

Download

This repository mirrors the checkpoints/ layout that config.py expects. Download it into the root of the code repository. Keep --include; without it this model card overwrites the code repository's README.md.

git clone https://github.com/Peteroos/FORCE-Interior.git
cd FORCE-Interior
hf download Peteroos/FORCE-Interior --include "checkpoints/*" --local-dir .

Model details

PFGM++ prior — checkpoints/model_028.pth

• Unconditional PFGM++ prior on 512 × 512 full-dose CT slices from 8 patients of the 2016 AAPM/Mayo Low Dose CT Grand Challenge: L067, L096, L109, L143, L192, L286, L291, L310. The test patients L333 and L506 were not used for training.
• Pixel domain: HU clipped to [-1024, 3071] and mapped linearly to [-1, 1].
• Architecture: the U-Net in model/ with num_res_blocks=3, channel_mult=1,2,4,8,16 (290 M parameters).
• Contents: {"model": state_dict, "ema": [tensors in model.parameters() order]}. Sample with the EMA weights; load_checkpoint() in run_force.py does this.

ROI-CT-CNN — checkpoints/roi_ct_cnn/net-epoch-200.pth

• U-Net post-processing of the truncated FBP (Han & Ye, Med. Phys. 2019), trained by us on full-dose Mayo CT at an ROI radius of 128 px for 200 epochs.
• Network: baselines/roi_ct_cnn/unet.py (net_type="type1", 64 base channels); attenuation is scaled so that water = 1.0.
• Contents: {"state_dict", "opt", "epoch", "net_type"}.
• The training/validation patient split of this checkpoint was not recorded.

DDS prior — checkpoints/dds_pretrained/lodochallenge_pixel_diffusion/

• Unmodified copy of jiayangshi/lodochallenge_pixel_diffusion (revision b7e291c2, sha256 of the U-Net weights verified), released under the MIT license with DM4CT (ICLR 2026). It is mirrored here only so that all baselines can be fetched from one place; the upstream model card is kept in the folder. Please cite DM4CT when using it.
• According to the DM4CT split, L333 is in this prior's training set and L506 in its test set.
• Intensity normalisation used by our adapter: baselines/dds/dm4ct_norm.json.

DRUNet — checkpoints/drunet/drunet_mayo.pth

• DRUNet (Zhang et al., DPIR, TPAMI 2021) with a noise-level map as second input; channels (64, 128, 256, 512), 4 residual blocks per level, 32.6 M parameters.
• Trained on 1369 full-dose slices from 6 patients (L109, L143, L192, L286, L291, L310). The validation patients (L067, L096) and test patients (L333, L506) were excluded.
• Same [-1, 1] pixel domain as the PFGM++ prior; additive Gaussian noise with σ ~ U[0, 0.08] (0–164 HU); 30 000 iterations.
• Contents: {"state_dict", "iters", "patients", "sigma_max"}.

Training data

All models were trained on the 2016 AAPM/Mayo Low Dose CT Grand Challenge data. No CT images or data derived from them are distributed in this repository.

License

The PFGM++ prior, ROI-CT-CNN and DRUNet weights are released under CC BY-NC 4.0. The mirrored DDS prior in checkpoints/dds_pretrained/ keeps its upstream MIT license.

Citation

@article{chen2026forceinterior,
  title  = {{FORCE-Interior}: Measurement-Consistent Adaptation of a Poisson-Flow
            Generative Prior for Interior {CT}},
  author = {Chen, Kang and Xia, Wenjun and Wang, Jianxu and Nafee, Mahmud Wasif
            and Wang, Ge},
  note   = {Manuscript under review},
  year   = {2026}
}