3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation

TL;DR

This paper introduces 3DGR-CAR, a novel method that reconstructs coronary arteries in 3D from only two sparse X-ray views using a 3D Gaussian representation, improving accuracy and efficiency.

Contribution

The paper proposes a 3D Gaussian representation and a Gaussian center predictor to enable fast, accurate coronary artery reconstruction from ultra-sparse X-ray views.

Findings

Outperforms existing methods in voxel accuracy

Achieves high-quality visual reconstruction from only 2 views

Demonstrates robustness on multiple datasets

Abstract

Reconstructing 3D coronary arteries is important for coronary artery disease diagnosis, treatment planning and operation navigation. Traditional reconstruction techniques often require many projections, while reconstruction from sparse-view X-ray projections is a potential way of reducing radiation dose. However, the extreme sparsity of coronary arteries in a 3D volume and ultra-limited number of projections pose significant challenges for efficient and accurate 3D reconstruction. To this end, we propose 3DGR-CAR, a 3D Gaussian Representation for Coronary Artery Reconstruction from ultra-sparse X-ray projections. We leverage 3D Gaussian representation to avoid the inefficiency caused by the extreme sparsity of coronary artery data and propose a Gaussian center predictor to overcome the noisy Gaussian initialization from ultra-sparse view projections. The proposed scheme enables fast and accurate 3D coronary artery reconstruction with only 2 views. Experimental results on two datasets indicate that the proposed approach significantly outperforms other methods in terms of voxel accuracy and visual quality of coronary arteries. The code will be available in https://github.com/windrise/3DGR-CAR.