DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or Mono-planar 2D X-ray(s)

TL;DR

This paper introduces DX2CT, a diffusion-based model that reconstructs high-quality 3D CT images from one or two 2D X-ray images using a novel transformer to incorporate spatial information.

Contribution

The paper presents a new conditional diffusion model with a transformer that effectively uses 2D X-ray data to reconstruct 3D CT volumes, outperforming existing methods.

Findings

DX2CT achieves superior reconstruction quality on benchmark datasets.

The transformer effectively encodes 3D spatial information from 2D X-ray images.

The method reduces radiation exposure while maintaining high-resolution imaging.

Abstract

Computational tomography (CT) provides high-resolution medical imaging, but it can expose patients to high radiation. X-ray scanners have low radiation exposure, but their resolutions are low. This paper proposes a new conditional diffusion model, DX2CT, that reconstructs three-dimensional (3D) CT volumes from bi or mono-planar X-ray image(s). Proposed DX2CT consists of two key components: 1) modulating feature maps extracted from two-dimensional (2D) X-ray(s) with 3D positions of CT volume using a new transformer and 2) effectively using the modulated 3D position-aware feature maps as conditions of DX2CT. In particular, the proposed transformer can provide conditions with rich information of a target CT slice to the conditional diffusion model, enabling high-quality CT reconstruction. Our experiments with the bi or mono-planar X-ray(s) benchmark datasets show that proposed DX2CT outperforms several state-of-the-art methods. Our codes and model will be available at: https://www.github.com/intyeger/DX2CT.