most citedLearning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

eess.IV2025

Cross-view Generalized Diffusion Model for Sparse-view CT Reconstruction

Jixiang Chen, Yiqun Lin, Yi Qin +2

Sparse-view computed tomography (CT) reduces radiation exposure by subsampling projection views, but conventional reconstruction methods produce severe streak artifacts with unders…

eess.IV2025

DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction

Yiqun Lin, Jixiang Chen, Hualiang Wang +4

Cone-beam computed tomography (CBCT) is a critical 3D imaging technology in the medical field, while the high radiation exposure required for high-quality imaging raises significan…

eess.IV2024

Spatial-Division Augmented Occupancy Field for Bone Shape Reconstruction from Biplanar X-Rays

Jixiang Chen, Yiqun Lin, Haoran Sun +1

Retrieving 3D bone anatomy from biplanar X-ray images is crucial since it can significantly reduce radiation exposure compared to traditional CT-based methods. Although various dee…

eess.IV20241 cited

Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction

Yiqun Lin, Hualiang Wang, Jixiang Chen +1

Cone-Beam Computed Tomography (CBCT) is an indispensable technique in medical imaging, yet the associated radiation exposure raises concerns in clinical practice. To mitigate these…

cs.LG2024

Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation

Marawan Elbatel, Hualiang Wang, Jixiang Chen +2

Federated semi-supervised learning (FedSemi) refers to scenarios where there may be clients with fully labeled data, clients with partially labeled, and even fully unlabeled client…