<div align=center> <img src="pics/deepsparse.png" alt="DeepSparse"> <h1>DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction</h1> </div> <div align=center> <a href="https://ieeexplore.ieee.org/document/11436116"> <img src="https://img.shields.io/badge/%F0%9F%93%96-TMI_2026-red.svg?style=flat-square"> </a> <a href="https://xmengli.github.io/"> <img src="https://img.shields.io/badge/%F0%9F%9A%80-xmed_Lab-ed6c00.svg?style=flat-square"> </a> </div> <br>

This repository contains the official implementation of DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction, accepted in IEEE Transactions on Medical Imaging (TMI), 2026.

For any questions regarding this repository, please contact jchenhu@connect.ust.hk.

:rocket: Updates

The code for pretraining, finetuning, and evaluation is released.
Pretrained and finetuned model weights are released on HuggingFace.

:star: Highlights

Foundation model for sparse-view CBCT: DeepSparse is pretrained on a large-scale abdominal CT dataset (AtlasAbdomenMini, ~7k volumes) and can be finetuned to diverse anatomical targets with few labelled examples.
Implicit neural representation with vector quantization: A codebook-based encoder-decoder reconstructs 3D volumes from sparse 2D X-ray projections, enabling memory-efficient inference at arbitrary resolution.
Two-stage finetuning: Stage 1 adapts the pretrained backbone to a target anatomy; Stage 2 refines the fine detail decoder with a frozen encoder, enabling high-quality reconstruction with as few as 6 views.
Generalizes across anatomies: Evaluated on abdomen (PANORAMA), pelvis (PENGWIN), lung (LUNA16), and tooth (ToothFairy) with consistent state-of-the-art performance.

:hammer: Environment

git clone https://github.com/xmed-lab/DeepSparse.git
cd DeepSparse
conda create -n deepsparse python=3.9
conda activate deepsparse
# PyTorch 1.13.1 + CUDA 11.6
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
# Other dependencies
pip install SimpleITK scipy numpy einops tqdm matplotlib pytorch-lightning timm
# TIGRE (cone-beam CT projector) — follow the instructions at:
# https://github.com/CERN/TIGRE

:computer: Prepare Dataset

All dataset preprocessing scripts are in data/. See data/README.md for the full directory structure and shared utilities.

Each dataset has its own subfolder with a README.md:

| Dataset | Anatomy | Folder | |---------|---------|--------| | AbdomenAtlas1.0Mini | Abdomen (pretraining) | data/atlas-mini/ | | PANORAMA | Abdomen (finetuning) | data/PANORAMA/ | | PENGWIN | Pelvis (finetuning) | data/PENGWIN/ | | LUNA16 | Lung (finetuning) | data/LUNA16_v2/ | | ToothFairy | Tooth (finetuning) | data/ToothFairy/ |

:key: Training & Evaluation

Tip: You can skip training entirely and download pretrained weights for direct inference — see Download Checkpoints below.

Pretraining

bash scripts/pretrain.sh

Trains the foundation model on AtlasAbdomenMini for 800 epochs. The checkpoint used for finetuning is saved at logs/pretrain/ep_700.pth.

Finetuning — Stage 1

bash scripts/finetune_s1.sh

Adapts the pretrained backbone to each downstream anatomy (abdomen, pelvis, lung, tooth) across three sparsity regimes (6, 8, 10 views). Checkpoints are saved under logs/{dataset}+{n_view}v+s1/.

Finetuning — Stage 2

bash scripts/finetune_s2.sh

Refines with a frozen encoder to sharpen reconstruction quality. Loads Stage 1 checkpoints and saves results under logs/{dataset}+{n_view}v+s2/.

Evaluation

bash scripts/evaluate_s2.sh

Runs evaluation on all datasets and view counts using Stage 2 checkpoints at logs/{dataset}+{n_view}v+s2/ep_400.pth.

:inbox_tray: Download Checkpoints

Pretrained and finetuned checkpoints are hosted on HuggingFace and can be downloaded for direct inference:

HuggingFace — HajihajihaJimmy/DeepSparse

Place the downloaded logs/ folder in the project root. See logs/README.md for a description of each checkpoint.

To download with huggingface-cli:

pip install huggingface-hub hf-transfer
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download HajihajihaJimmy/DeepSparse --repo-type dataset --local-dir ./logs

:blue_book: Citation

If you find this work helpful, please cite:

@ARTICLE{11436116,
  author={Lin, Yiqun and Chen, Jixiang and Wang, Hualiang and Yang, Jiewen and Guo, Jiarong and Zhang, Yi and Li, Xiaomeng},
  journal={IEEE Transactions on Medical Imaging},
  title={DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction},
  year={2026},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TMI.2026.3674948}
}

:beers: Acknowledgements

We thank the challenge organizers of PANORAMA, PENGWIN, LUNA16, and ToothFairy for making their datasets publicly available.

DeepSparse

Install / Use

README