313 citations · 620 across the 26 of their papers we have counts for
26 papers
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…
CMOS based high-resolution dynamic X-ray imaging with inorganic perovskite
Yanliang Liu, Chaosong Gao, Jiongtao Zhu +13
High-resolution dynamic X-ray detector is crucial for time-resolved digital radiography (DR) imaging and fast 3D medical computed tomography (CT) imaging. Recently, perovskites hav…
Dual-domain Attention-based Deep Network for Sparse-view CT Artifact Reduction
Xiang Gao, Ting Su, Jiongtao Zhu +7
Due to the wide applications of X-ray computed tomography (CT) in medical imaging activities, radiation exposure has become a major concern for public health. Sparse-view CT is a p…
Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework
Chen Hu, Cheng Li, Haifeng Wang +3
Image reconstruction from undersampled k-space data plays an important role in accelerating the acquisition of MR data, and a lot of deep learning-based methods have been exploited…
High temporal resolution total-body dynamic PET imaging based on pixel-level time-activity curve correction
Zixiang Chen, Yaping Wu, Na Zhang +5
Dynamic positron emission tomography (dPET) is currently a widely used medical imaging technique for the clinical diagnosis, staging and therapy guidance of all kinds of human canc…
Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network
Kehan Qi, Haoran Li, Chuyu Rong +4
Image Quality Assessment (IQA) is of great value in the workflow of Magnetic Resonance Imaging (MRI)-based analysis. Blind IQA (BIQA) methods are especially required since high-qua…
Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data
Shanshan Wang, Taohui Xiao, Qiegen Liu +1
Magnetic resonance imaging is a powerful imaging modality that can provide versatile information but it has a bottleneck problem "slow imaging speed". Reducing the scanned measurem…
Annotation-efficient deep learning for automatic medical image segmentation
Shanshan Wang, Cheng Li, Rongpin Wang +12
Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training…
Multi-task MR Imaging with Iterative Teacher Forcing and Re-weighted Deep Learning
Kehan Qi, Yu Gong, Xinfeng Liu +3
Noises, artifacts, and loss of information caused by the magnetic resonance (MR) reconstruction may compromise the final performance of the downstream applications. In this paper,…
DIRECT-Net: a unified mutual-domain material decomposition network for quantitative dual-energy CT imaging
Ting Su, Xindong Sun, Yikun Zhang +7
By acquiring two sets of tomographic measurements at distinct X-ray spectra, the dual-energy CT (DECT) enables quantitative material-specific imaging. However, the conventionally d…
DIR-DBTnet: Deep iterative reconstruction network for 3D digital breast tomosynthesis imaging
Ting Su, Xiaolei Deng, Zhenwei Wang +5
Purpose: The goal of this study is to develop a novel deep learning (DL) based reconstruction framework to improve the digital breast tomosynthesis (DBT) imaging performance. Metho…
A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint
Weijian Huang, Hao Yang, Xinfeng Liu +5
Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless,…
Deep Low-rank Prior in Dynamic MR Imaging
Ziwen Ke, Wenqi Huang, Jing Cheng +8
The deep learning methods have achieved attractive performance in dynamic MR cine imaging. However, all of these methods are only driven by the sparse prior of MR images, while the…
Estimation of angular sensitivity for X-ray interferometers with multiple phase gratings
Jianwei Chen, Jiecheng Yang, Peiping Zhu +5
Recently, X-ray interferometers with more than one phase grating have been developed for differential phase contrast (DPC) imaging. In this study, a novel framework is developed to…
An Unsupervised Deep Learning Method for Multi-coil Cine MRI
Ziwen Ke, Jing Cheng, Leslie Ying +3
Deep learning has achieved good success in cardiac magnetic resonance imaging (MRI) reconstruction, in which convolutional neural networks (CNNs) learn a mapping from the undersamp…
LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset
Shanshan Wang, Yanxia Chen, Taohui Xiao +3
This paper proposes to learn analysis transform network for dynamic magnetic resonance imaging (LANTERN) with small dataset. Integrating the strength of CS-MRI and deep learning, t…
Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation
Cheng Li, Hui Sun, Zaiyi Liu +3
Multi-modal magnetic resonance imaging (MRI) is essential in clinics for comprehensive diagnosis and surgical planning. Nevertheless, the segmentation of multi-modal MR images tend…
CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke
Hao Yang, Weijian Huang, Kehan Qi +5
Segmenting stroke lesions from T1-weighted MR images is of great value for large-scale stroke rehabilitation neuroimaging analyses. Nevertheless, there are great challenges with th…
Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning
Jing Cheng, Haifeng Wang, Yanjie Zhu +10
Medical imaging is playing a more and more important role in clinics. However, there are several issues in different imaging modalities such as slow imaging speed in MRI, radiation…
T1rho Fractional-order Relaxation of Human Articular Cartilage
Lixian Zou, Haifeng Wang, Yanjie Zhu +8
T1rho imaging is a promising non-invasive diagnostic tool for early detection of articular cartilage degeneration. A mono-exponential model is normally used to describe the T1rho r…
Automatic image-domain Moire artifact reduction method in grating-based x-ray interferometry imaging
Jianwei Chen, Jiongtao Zhu, Wei Shi +3
The aim of this study is to demonstrate the feasibility of removing the image Moire artifacts caused by system inaccuracies in grating-based x-ray interferometry imaging system via…
CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint
Ziwen Ke, Shanshan Wang, Huitao Cheng +4
Dynamic magnetic resonance (MR) imaging has generated great research interest, as it can provide both spatial and temporal information for clinical diagnosis. However, slow imaging…
DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training
Shanshan Wang, Ziwen Ke, Huitao Cheng +4
Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction pro…
AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
Hui Sun, Cheng Li, Boqiang Liu +3
Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to t…