DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction

This repository contains the official implementation of "DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction", published at NeurIPS 2024.

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Overview

DiffusionBlend introduces a novel method for 3D computed tomography (CT) reconstruction using position-aware diffusion score blending. By leveraging position-specific priors, the framework achieves enhanced reconstruction accuracy while maintaining computational efficiency.

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Features

• Position-aware Diffusion Blending: Incorporates spatial information to refine 3D reconstruction quality.
• Triplane-based 3D Representation: Utilizes a position encoding to model 3D patch priors efficiently.
• Scalable and Generalizable: Designed for both synthetic and real-world CT reconstruction tasks.

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Requirements

The code is implemented in Python and requires the following dependencies:

• torch (>=1.9.0)
• torchvision
• numpy

You can install the dependencies via:

pip install torch torchvision numpy

Training

To train the model on synthetic volume CT data, use the following script:

bash train_SVCT_3D_triplane.sh

Inference

To perform inference and evaluate 3D reconstruction using diffusion score blending, use:

bash eval_3D_blend_cond.sh

Citation

If you find this work useful in your research, please cite:

@inproceedings{diffusionblend2024, title={DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction}, author={Your Name and Collaborators}, booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, year={2024} }

Acknowledgements

We thank the contributors and the NeurIPS community for their valuable feedback and discussions.

License

This project is licensed under the MIT License. See the LICENSE file for details.