104 citations · 166 across the 11 of their papers we have counts for
12 papers
Improving 2D Diffusion Models for 3D Medical Imaging with Inter-Slice Consistent Stochasticity
Chenhe Du, Qing Wu, Xuanyu Tian +3
3D medical imaging is in high demand and essential for clinical diagnosis and scientific research. Currently, diffusion models (DMs) have become an effective tool for medical imagi…
XDen-1K: A Density Field Dataset of Real-World Objects
Jingxuan Zhang, Tianqi Yu, Yatu Zhang +6
A deep understanding of the physical world is essential for robotic manipulation and physically realistic simulation. While current methods, including VLM-based and other learning-…
SPIDER: Structure-Preferential Implicit Deep Network for Biplanar X-ray Reconstruction
Tianqi Yu, Xuanyu Tian, Jiawen Yang +4
Biplanar X-ray imaging is widely used in health screening, postoperative rehabilitation evaluation of orthopedic diseases, and injury surgery due to its rapid acquisition, low radi…
JSolver: Joint Spectrum Estimation and Multi-Material Decomposition from Single-Energy CT Projections
Qing Wu, Hongjiang Wei, Jingyi Yu +2
Multi-material decomposition (MMD) enables quantitative reconstruction of tissue compositions in the human body, supporting a wide range of clinical applications. However, traditio…
Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT
Qing Wu, Hongjiang Wei, Jingyi Yu +1
Ring artifacts are prevalent in 3D cone-beam computed tomography (CBCT) due to non-ideal responses of X-ray detectors, substantially affecting image quality and diagnostic reliabil…
Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation
Qing Wu, Chenhe Du, Xuanyu Tian +3
Motion correction (MoCo) in radial MRI is a particularly challenging problem due to the unpredictability of subject movement. Current state-of-the-art (SOTA) MoCo algorithms often…
Solving Energy-Independent Density for CT Metal Artifact Reduction via Neural Representation
Qing Wu, Xu Guo, Lixuan Chen +8
X-ray CT often suffers from shadowing and streaking artifacts in the presence of metallic materials, which severely degrade imaging quality. Physically, the linear attenuation coef…
DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction
Chenhe Du, Xiyue Lin, Qing Wu +9
Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data…
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
Qing Wu, Lixuan Chen, Ce Wang +4
Emerging neural reconstruction techniques based on tomography (e.g., NeRF, NeAT, and NeRP) have started showing unique capabilities in medical imaging. In this work, we present a n…
Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field
Qing Wu, Xin Li, Hongjiang Wei +2
Neural Radiance Field (NeRF) has widely received attention in Sparse-View Computed Tomography (SVCT) reconstruction tasks as a self-supervised deep learning framework. NeRF-based S…
Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography
Qing Wu, Ruimin Feng, Hongjiang Wei +2
In the present work, we propose a Self-supervised COordinate Projection nEtwork (SCOPE) to reconstruct the artifacts-free CT image from a single SV sinogram by solving the inverse…
An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation
Qing Wu, Yuwei Li, Yawen Sun +4
High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In MRI, restricted by hardware capacity, scan time, and pa…