53 citations · 68 across the 15 of their papers we have counts for
11 papers · 1 filter
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…
Real-Time Reconstruction of 3D Bone Models via Very-Low-Dose Protocols
Yiqun Lin, Haoran Sun, Yongqing Li +12
Patient-specific bone models are essential for designing surgical guides and preoperative planning, as they enable the visualization of intricate anatomical structures. However, tr…
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…
CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videos
Jiewen Yang, Yiqun Lin, Bin Pu +3
Echocardiogram video plays a crucial role in analysing cardiac function and diagnosing cardiac diseases. Current deep neural network methods primarily aim to enhance diagnosis accu…
Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images
Hualiang Wang, Yiqun Lin, Xinpeng Ding +1
General networks for 3D medical image segmentation have recently undergone extensive exploration. Behind the exceptional performance of these networks lies a significant demand for…
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…
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…
C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction
Yiqun Lin, Jiewen Yang, Hualiang Wang +3
Cone beam computed tomography (CBCT) is an important imaging technology widely used in medical scenarios, such as diagnosis and preoperative planning. Using fewer projection views…
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models
Tianqi Xiang, Wenjun Yue, Yiqun Lin +3
Performing magnetic resonance imaging (MRI) reconstruction from under-sampled k-space data can accelerate the procedure to acquire MRI scans and reduce patients' discomfort. The re…
Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction
Yiqun Lin, Zhongjin Luo, Wei Zhao +1
Sparse-view cone-beam CT (CBCT) reconstruction is an important direction to reduce radiation dose and benefit clinical applications. Previous voxel-based generation methods represe…
Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels
Wanqin Ma, Huifeng Yao, Yiqun Lin +2
Developing a deep learning method for medical segmentation tasks heavily relies on a large amount of labeled data. However, the annotations require professional knowledge and are l…