427 citations · 3.3k across the 77 of their papers we have counts for
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Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction
Chenglong Ma, Zilong Li, Yuanlin Li +5
Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained…
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…
HOPE: Hybrid-granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment
Chenhui Wang, Yiming Lei, Tao Chen +3
Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease (AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtyp…
Prompted Contextual Transformer for Incomplete-View CT Reconstruction
Chenglong Ma, Zilong Li, Junjun He +3
Incomplete-view computed tomography (CT) can shorten the data acquisition time and allow scanning of large objects, including sparse-view and limited-angle scenarios, each with var…
Learning to Distill Global Representation for Sparse-View CT
Zilong Li, Chenglong Ma, Jie Chen +2
Sparse-view computed tomography (CT) -- using a small number of projections for tomographic reconstruction -- enables much lower radiation dose to patients and accelerated data acq…
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…
FreeSeed: Frequency-band-aware and Self-guided Network for Sparse-view CT Reconstruction
Chenglong Ma, Zilong Li, Junping Zhang +2
Sparse-view computed tomography (CT) is a promising solution for expediting the scanning process and mitigating radiation exposure to patients, the reconstructed images, however, c…
FAN-Net: Fourier-Based Adaptive Normalization For Cross-Domain Stroke Lesion Segmentation
Weiyi Yu, Yiming Lei, Hongming Shan
Since stroke is the main cause of various cerebrovascular diseases, deep learning-based stroke lesion segmentation on magnetic resonance (MR) images has attracted considerable atte…
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
Qi Gao, Zilong Li, Junping Zhang +2
Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. Recently, some works have attempted to use diffusion models…
LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring
Zhihao Chen, Chuang Niu, Qi Gao +2
This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep learning methods were developed in this context, typically they focus on 2D images and perfor…
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…
SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization
Weiyi Yu, Zhizhong Huang, Junping Zhang +1
There are considerable interests in automatic stroke lesion segmentation on magnetic resonance (MR) images in the medical imaging field, as stroke is an important cerebrovascular d…
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…
Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey
Wenjun Xia, Hongming Shan, Ge Wang +1
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big da…
Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography
Hongming Shan, Rodrigo de Barros Vimieiro, Lucas Rodrigues Borges +2
Digital mammography is still the most common imaging tool for breast cancer screening. Although the benefits of using digital mammography for cancer screening outweigh the risks as…
DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising
Zhizhong Huang, Junping Zhang, Yi Zhang +1
LDCT has drawn major attention in the medical imaging field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing the radiation dose, however, de…
Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography
Hanqing Chao, Hongming Shan, Fatemeh Homayounieh +7
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an op…
Stabilizing Deep Tomographic Reconstruction
Weiwen Wu, Dianlin Hu, Wenxiang Cong +7
Tomographic image reconstruction with deep learning is an emerging field, but a recent landmark study reveals that several deep reconstruction networks are unstable for computed to…
Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction
Chuang Niu, Wenxiang Cong, Fenglei Fan +4
Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for training. As synthesiz…
Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network
Qing Lyu, Hongming Shan, Yibin Xie +2
Cine cardiac magnetic resonance imaging (MRI) is widely used for diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As co…
Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction
Huidong Xie, Hongming Shan, Wenxiang Cong +5
Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density di…
Deep Encoder-decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-view Data
Huidong Xie, Hongming Shan, Ge Wang
X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizing X-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiatio…
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising
Yu Gong, Hongming Shan, Yueyang Teng +5
Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However…
Multi-Contrast Super-Resolution MRI Through a Progressive Network
Qing Lyu, Hongming Shan, Ge Wang
Magnetic resonance imaging (MRI) is widely used for screening, diagnosis, image-guided therapy, and scientific research. A significant advantage of MRI over other imaging modalitie…