10 citations · 10 across the 6 of their papers we have counts for
4 papers · 1 filter
Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
Qi Gao, Zhihao Chen, Dong Zeng +3
The generalization of deep learning-based low-dose computed tomography (CT) reconstruction models to doses unseen in the training data is important and remains challenging. Previou…
Continuous Filtered Backprojection by Learnable Interpolation Network
Hui Lin, Dong Zeng, Qi Xie +3
Accurate reconstruction of computed tomography (CT) images is crucial in medical imaging field. However, there are unavoidable interpolation errors in the backprojection step of th…
SS-CTML: Self-Supervised Cross-Task Mutual Learning for CT Image Reconstruction
Gaofeng Chen, Yaoduo Zhang, Li Huang +5
Supervised deep-learning (SDL) techniques with paired training datasets have been widely studied for X-ray computed tomography (CT) image reconstruction. However, due to the diffic…
Direct Energy-resolving CT Imaging via Energy-integrating CT images using a Unified Generative Adversarial Network
Lisha Yao, Sui Li, Manman Zhu +3
Energy-resolving computed tomography (ErCT) has the ability to acquire energy-dependent measurements simultaneously and quantitative material information with improved contrast-to-…