用3D高斯表示法，仅凭2张二维造影图重建冠状动脉，精度显著提升。
3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation
- 采用3D高斯表示，解决稀疏投影下数据极稀疏的难题。
- 仅需2个视角投影，重建误差比现有方法降低37.6%。
- 适合心血管影像分析与低剂量介入手术导航场景。
冠状动脉三维重建对心脏病诊断、治疗规划和手术导航至关重要。传统方法通常需要大量投影图像，而从稀疏视角的X射线投影进行重建可有效降低辐射剂量。然而，冠状动脉在三维空间中极度稀疏，且投影数量极为有限，给高效准确的三维重建带来巨大挑战。为此，本文提出3DGR-CAR，一种基于3D高斯表示的冠状动脉重建方法，用于超稀疏视角下的重建。该方法利用3D高斯表示克服因数据极度稀疏导致的计算效率低下问题，并设计高斯中心预测器，缓解超稀疏视角下噪声严重的初始高斯分布问题。所提方案仅需2个视角投影即可实现快速且高精度的三维冠状动脉重建。在两个数据集上的实验表明，该方法在体素精度和血管视觉质量方面显著优于其他方法。代码将公开于https://github.com/windrise/3DGR-CAR。
原文摘要 · Abstract (English)
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.
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