12 citations · 31 across the 9 of their papers we have counts for
11 papers
X-ray Dissectography Improves Lung Nodule Detection
Chuang Niu, Giridhar Dasegowda, Pingkun Yan +2
Although radiographs are the most frequently used worldwide due to their cost-effectiveness and widespread accessibility, the structural superposition along the x-ray paths often r…
Convolutional Neural Network to Restore Low-Dose Digital Breast Tomosynthesis Projections in a Variance Stabilization Domain
Rodrigo de Barros Vimieiro, Chuang Niu, Hongming Shan +3
Digital breast tomosynthesis (DBT) exams should utilize the lowest possible radiation dose while maintaining sufficiently good image quality for accurate medical diagnosis. In this…
AI-Enabled Ultra-Low-Dose CT Reconstruction
Weiwen Wu, Chuang Niu, Shadi Ebrahimian +3
By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose CT reconstruction is a holy grail to minimize cancer risks and genetic damages, especially for children. Wi…
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning
Arjun Krishna, Kedar Bartake, Chuang Niu +4
Deep learning has shown great promise for CT image reconstruction, in particular to enable low dose imaging and integrated diagnostics. These merits, however, stand at great odds w…
Task-Oriented Low-Dose CT Image Denoising
Jiajin Zhang, Hanqing Chao, Xuanang Xu +3
The extensive use of medical CT has raised a public concern over the radiation dose to the patient. Reducing the radiation dose leads to increased CT image noise and artifacts, whi…
Noise Entangled GAN For Low-Dose CT Simulation
Chuang Niu, Ge Wang, Pingkun Yan +8
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image. First, we present two schemes to generate a clean CT…