27 citations · 53 across the 6 of their papers we have counts for
6 papers
FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising
Zhihao Chen, Qi Gao, Zilong Li +4
Low-dose computed tomography (CT) denoising is crucial for reduced radiation exposure while ensuring diagnostically acceptable image quality. Despite significant advancements drive…
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
Zhihao Chen, Tao Chen, Chenhui Wang +5
Low-dose computed tomography (LDCT) reduces radiation exposure but often degrades image quality, potentially compromising diagnostic accuracy. Existing deep learning-based denoisin…
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…
Low-resolution Prior Equilibrium Network for CT Reconstruction
Yijie Yang, Qifeng Gao, Yuping Duan
The unrolling method has been investigated for learning variational models in X-ray computed tomography. However, it has been observed that directly unrolling the regularization mo…
ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising
Zhihao Chen, Qi Gao, Yi Zhang +1
While various deep learning methods have been proposed for low-dose computed tomography (CT) denoising, most of them leverage the normal-dose CT images as the ground-truth to super…
Quad-Net: Quad-domain Network for CT Metal Artifact Reduction
Zilong Li, Qi Gao, Yaping Wu +5
Metal implants and other high-density objects in patients introduce severe streaking artifacts in CT images, compromising image quality and diagnostic performance. Although various…