85 citations · 122 across the 12 of their papers we have counts for
12 papers
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…
Lesion classification by model-based feature extraction: A differential affine invariant model of soft tissue elasticity
Weiguo Cao, Marc J. Pomeroy, Zhengrong Liang +9
The elasticity of soft tissues has been widely considered as a characteristic property to differentiate between healthy and vicious tissues and, therefore, motivated several elasti…
Predicting the risk of pancreatic cancer with a CT-based ensemble AI algorithm
Chenjie Zhou MD, Jianhua Ma Ph. D, Xiaoping Xu MD +9
Objectives: Pancreatic cancer is a lethal disease, hard to diagnose and usually results in poor prognosis and high mortality. Developing an artificial intelligence (AI) algorithm t…
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-…
Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
Shulong Li, Panpan Xu, Bin Li +8
To predict lung nodule malignancy with a high sensitivity and specificity, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the ou…
Radon Inversion via Deep Learning
Ji He, Jianhua Ma
Radon transform is widely used in physical and life sciences and one of its major applications is the X-ray computed tomography (X-ray CT), which is significant in modern health ex…
Unsupervised/Semi-supervised Deep Learning for Low-dose CT Enhancement
Mingrui Geng, Yun Deng, Qian Zhao +4
Recently, deep learning(DL) methods have been proposed for the low-dose computed tomography(LdCT) enhancement, and obtain good trade-off between computational efficiency and image…
Pseudo Dual Energy CT Imaging using Deep Learning Based Framework: Initial Study
Sui Li, Yongbo Wang, Yuting Liao +4
Dual energy computed tomography (DECT) has become of particular interest in clinic recent years. The DECT scan comprises two images, corresponding to two photon attenuation coeffic…
Characterization of the previous normal-dose CT scan induced nonlocal means regularization method for low-dose CT image reconstruction
Hao Zhang, Jianhua Ma, William Moore +1
Repeated computed tomography (CT) scans are required in some clinical applications such as image-guided radiotherapy and follow-up observations over a time period. To optimize the…
Statistical models and regularization strategies in statistical image reconstruction of low-dose X-ray CT: a survey
Hao Zhang, Jing Wang, Jianhua Ma +2
Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose X-ray computed tomography (CT) as compared to the convent…