bibtext: @InProceedings{ Fu_3DGRCAR_MICCAI2024,
                   author = { Fu, Xueming and Li, Yingtai and Tang, Fenghe and Li, Jun and Zhao, Mingyue and Teng, Gao-Jun and Zhou, S. Kevin },
                   title = { { 3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
abstract: Reconstructing 3D coronary arteries is important for coronary artery disease diagnosis, treatment planning and operation navigation. Traditional 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 artery volume and ultra-limited number of projections pose significant challenges for efficient and accurate 3D reconstruction. 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 arteries 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 artery.
title: 3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation
authors: Fu, Xueming Li, Yingtai Tang, Fenghe Li, Jun Zhao, Mingyue Teng, Gao-Jun Zhou, S. Kevin