5 citations · 9 across the 11 of their papers we have counts for
9 papers
3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model
Chuang Niu, Christopher Wiedeman, Mengzhou Li +2
This study aims to improve photon counting CT (PCCT) image resolution using denoising diffusion probabilistic models (DDPM). Although DDPMs have shown superior performance when app…
CT-based Anomaly Detection of Liver Tumors Using Generative Diffusion Prior
Yongyi Shi, Chuang Niu, Amber L. Simpson +3
CT is a main modality for imaging liver diseases, valuable in detecting and localizing liver tumors. Traditional anomaly detection methods analyze reconstructed images to identify…
Low-dose CT Denoising with Language-engaged Dual-space Alignment
Zhihao Chen, Tao Chen, Chenhui Wang +3
While various deep learning methods were proposed for low-dose computed tomography (CT) denoising, they often suffer from over-smoothing, blurring, and lack of explainability. To a…
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation
Christopher Wiedeman, Chuang Niu, Mengzhou Li +3
Ultra-high resolution images are desirable in photon counting CT (PCCT), but resolution is physically limited by interactions such as charge sharing. Deep learning is a possible me…
Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT
Wenjun Xia, Yongyi Shi, Chuang Niu +2
Computed tomography (CT) involves a patient's exposure to ionizing radiation. To reduce the radiation dose, we can either lower the X-ray photon count or down-sample projection vie…
Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models
Qing Lyu, Josh Tan, Megan E. Lipford +6
Iodinated contrast media is essential for dual-energy computed tomography (DECT) angiography. Previous studies show that iodinated contrast media may cause side effects, and the in…