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

TL;DR

DIFR3CT is a novel 3D latent diffusion model that reconstructs plausible CT volumes from limited planar X-ray images, outperforming existing methods and enabling uncertainty quantification for clinical applications.

Contribution

The paper introduces DIFR3CT, a new probabilistic 3D CT reconstruction method from few X-rays using latent diffusion, with improved accuracy and uncertainty estimation capabilities.

Findings

Outperforms recent sparse CT reconstruction baselines in PSNR and SSIM.

Supports uncertainty quantification via Monte Carlo sampling.

Demonstrates feasibility in automated breast radiotherapy planning.

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. Our code is available at https://github.com/yransun/DIFR3CT.