4 citations · 4 across the 8 of their papers we have counts for
9 papers
An Uncertainty-Guided Digital Twin Framework for Online Adaptive Proton Therapy in Head and Neck Cancer: A Feasibility Study
Yizhou Wu, Ryan J. Sanford, Huiqiao Xie +12
Objective: Head and neck (HN) proton therapy spans six to seven weeks of anatomical change, while offline replanning takes about a week. We present an uncertainty-guided digital tw…
CBCT-Based Synthetic CT Generation Using Conditional Flow Matching Model
Junbo Peng, Huiqiao Xie, Tonghe Wang +2
Daily or weekly cone-beam computed tomography (CBCT) is employed in image-guided radiotherapy (IGRT) for precise patient alignment. However, its clinical utility in quantitative ta…
Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model
Qiang Li, Mojtaba Safari, Shansong Wang +4
Low-dose computed tomography (LDCT) reduces patient radiation exposure but introduces substantial noise that degrades image quality and hinders diagnostic accuracy. Existing denois…
Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models
Yuan Gao, Shaoyan Pan, Mingzhe Hu +6
Cone-beam CT (CBCT) is widely used in clinical radiotherapy for image-guided treatment, improving setup accuracy, adaptive planning, and motion management. However, slow gantry rot…
Spatiotemporal Gaussian Optimization for 4D Cone Beam CT Reconstruction from Sparse Projections
Yabo Fu, Hao Zhang, Weixing Cai +6
In image-guided radiotherapy (IGRT), four-dimensional cone-beam computed tomography (4D-CBCT) is critical for assessing tumor motion during a patients breathing cycle prior to beam…
Optimization-Based Image Reconstruction Regularized with Inter-Spectral Structural Similarity for Limited-Angle Dual-Energy Cone-Beam CT
Junbo Peng, Tonghe Wang, Huiqiao Xie +6
Background: Limited-angle (LA) dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without h…
Image-Domain Material Decomposition for Dual-energy CT using Unsupervised Learning with Data-fidelity Loss
Junbo Peng, Chih-Wei Chang, Huiqiao Xie +6
Background: Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise…
One-step Iterative Estimation of Effective Atomic Number and Electron Density for Dual Energy CT
Qian Wang, Huiqiao Xie, Tonghe Wang +7
Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification…
Deformable Image Registration using Unsupervised Deep Learning for CBCT-guided Abdominal Radiotherapy
Huiqiao Xie, Yang Lei, Yabo Fu +6
CBCTs in image-guided radiotherapy provide crucial anatomy information for patient setup and plan evaluation. Longitudinal CBCT image registration could quantify the inter-fraction…