12 citations · 23 across the 8 of their papers we have counts for
8 papers
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…
A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions
Hao Wang, Ruihong He, Xiaoyu Zhang +3
Deep learning techniques have been widely used in computed tomography (CT) but require large data sets to train networks. Moreover, data sharing among multiple institutions is limi…
Super resolution dual-energy cone-beam CT imaging with dual-layer flat-panel detector
Ting Su, Jiongtao Zhu, Xin Zhang +7
For medical cone-beam computed tomography (CBCT) imaging, the native receptor array of the flat-panel detector (FPD) is usually binned into a reduced matrix size. By doing so, the…
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-…