21 citations · 35 across the 5 of their papers we have counts for
5 papers
An Improved Iterative Neural Network for High-Quality Image-Domain Material Decomposition in Dual-Energy CT
Zhipeng Li, Yong Long, Il Yong Chun
Dual-energy computed tomography (DECT) has been widely used in many applications that need material decomposition. Image-domain methods directly decompose material images from high…
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
Siqi Ye, Zhipeng Li, Michael T. McCann +2
Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent machine learning methods for image reconstruction typ…
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction
Zhipeng Li, Siqi Ye, Yong Long +1
Recent years have witnessed growing interest in machine learning-based models and techniques for low-dose X-ray CT (LDCT) imaging tasks. The methods can typically be categorized in…
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
Zhipeng Li, Saiprasad Ravishankar, Yong Long +1
Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition ope…
Sparse-View X-Ray CT Reconstruction Using Prior with Learned Transform
Xuehang Zheng, Il Yong Chun, Zhipeng Li +2
A major challenge in X-ray computed tomography (CT) is reducing radiation dose while maintaining high quality of reconstructed images. To reduce the radiation dose, one can reduce…