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DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rays

Project Website

This is the official pytorch implementation of the deep leraning model DIFR3CT for 3D CT reconstruction from few planar X-rays using latent diffusion model.

Code release

The code will be released once the review process ends. In the meantime, if you have any questions regarding our paper, please feel free to open a new issue here or send me an email! Thanks for your patience :).

Abstract

Computed Tomography (CT) scans are the standard-of-care for the visualization and diagnosis of many clinical ailments, and are needed for the treatment planning of external beam radiotherapy. Unfortunately, the availability of CT scanners in low- and mid-resource settings is highly variable. Planar x-ray radiography units, in comparison, are far more prevalent, but can only provide limited 2D observations of the 3D anatomy. In this work we propose DIFR3CT, a 3D latent diffusion model, that can generate a distribution of plausible CT volumes from one or few ($<$ 10) planar x-ray observations. DIFR3CT works by fusing 2D features from each x-ray into a joint 3D space, and performing diffusion conditioned on these fused features in a low-dimensional latent space. We conduct extensive experiments demonstrating that DIFR3CT is better than recent sparse CT reconstruction baselines in terms of standard pixel-level (PSNR, SSIM) on both the public LIDC and in-house post-mastectomy CT datasets. We also show that DIFR3CT supports uncertainty quantification via Monte Carlo sampling, which provides an opportunity to measure reconstruction reliability. Finally, we perform a preliminary pilot study evaluating DIFR3CT for automated breast radiotherapy contouring and planning -- and demonstrate promising feasibility.

Citing our work

If you find the paper useful in your research, please cite the paper:

  @article{sun2024difr3ct,
           title={DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rays},
           author={Sun, Yiran and Baroudi, Hana and Netherton, Tucker and Court, Laurence and Mawlawi, Osama and Veeraraghavan, Ashok and Balakrishnan, Guha},
           journal={arXiv preprint arXiv:2408.15118},
           year={2024}
           }