1.7k citations · 2.5k across the 21 of their papers we have counts for
22 papers
Physics-informed Score-based Diffusion Model for Limited-angle Reconstruction of Cardiac Computed Tomography
Shuo Han, Yongshun Xu, Dayang Wang +5
Cardiac computed tomography (CT) has emerged as a major imaging modality for the diagnosis and monitoring of cardiovascular diseases. High temporal resolution is essential to ensur…
Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems
Zirong Li, Yanyang Wang, Jianjia Zhang +2
Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial technologies in the field of medical imaging. Score-based models have proven to be effective in addressing…
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…
TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising
Dayang Wang, Zhan Wu, Hengyong Yu
Low dose computed tomography is a mainstream for clinical applications. How-ever, compared to normal dose CT, in the low dose CT (LDCT) images, there are stronger noise and more ar…
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…
EZLDA: Efficient and Scalable LDA on GPUs
Shilong Wang, Hang Liu, Anil Gaihre +1
LDA is a statistical approach for topic modeling with a wide range of applications. However, there exist very few attempts to accelerate LDA on GPUs which come with exceptional com…
MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction
Haimiao Zhang, Baodong Liu, Hengyong Yu +1
X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis and image-guided interventions. In this paper, we propose a new deep learning based model f…
Improved Material Decomposition with a Two-step Regularization for spectral CT
Weiwen Wu, Peijun Chen, Vince Vardhanabhuti +2
One of the advantages of spectral computed tomography (CT) is it can achieve accurate material components using the material decomposition methods. The image-based material decompo…
On a Sparse Shortcut Topology of Artificial Neural Networks
Fenglei Fan, Dayang Wang, Hengtao Guo +4
In established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effe…
Block Matching Frame based Material Reconstruction for Spectral CT
Weiwen Wu, Qian Wang, Fenglin Liu +2
Spectral computed tomography (CT) has a great potential in material identification and decomposition. To achieve high-quality material composition images and further suppress the x…
Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstruction
Weiwen Wu, Fenglin Liu, Yanbo Zhang +2
Spectral computed tomography (CT) reconstructs material-dependent attenuation images with the projections of multiple narrow energy windows, it is meaningful for material identific…
Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary
Weiwen Wu, Yanbo Zhang, Qian Wang +3
Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applicat…
Convolutional Neural Network Based Metal Artifact Reduction in X-ray Computed Tomography
Yanbo Zhang, Hengyong Yu
In the presence of metal implants, metal artifacts are introduced to x-ray CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the pa…
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
Qingsong Yang, Pingkun Yan, Yanbo Zhang +5
In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein d…
Local Linear Constraint based Optimization Model for Dual Spectral CT
Qian Wang
Dual spectral computed tomography (DSCT) can achieve energy- and material-selective images, and has a superior distinguishability of some materials than conventional single spectra…
Deep Learning for the Classification of Lung Nodules
He Yang, Hengyong Yu, Ge Wang
Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging. Compared to the conventional machine learn…
BPF-type Region-of-interest Reconstruction for Parallel Translational Computed Tomography
Weiwen Wu, Hengyong Yu, Shaoyu Wang +1
Recently, an ultra-low-cost linear scan based tomography architecture was proposed by our team. Similar to linear tomosynthesis, the source and detector are translated in opposite…
Local Filtering Fundamentally Against Wide Spectrum
Ge Wang, Hengyong Yu, Scott S. Verbridge +1
Chen et al. (1) applied three-dimensional (3D) Fourier filtering together with equal-slope tomographic reconstruction for an observation of nearly all the atoms in a multiply twinn…
Top-level Design and Pilot Analysis of Low-end CT Scanners Based on Linear Scanning for Developing Countries
Fenglin Liu, Hengyong Yu, Wenxiang Cong +1
Purpose: The goal is to develop a new architecture for computed tomography (CT) which is at an ultra-low-dose for developing countries, especially in rural areas. Methods: The prop…
Dictionary-Learning-Based Reconstruction Method for Electron Tomography
Baodong Liu, Hengyong Yu, Scott S. Verbridge +2
Electron tomography usually suffers from so called missing wedge artifacts caused by limited tilt angle range. An equally sloped tomography (EST) acquisition scheme (which should b…
Meaning of Interior Tomography
Ge Wang, Hengyong Yu
The classic imaging geometry for computed tomography is for collection of un-truncated projections and reconstruction of a global image, with the Fourier transform as the theoretic…
Omni-tomography/Multi-tomography -- Integrating Multiple Modalities for Simultaneous Imaging
Ge Wang, Jie Zhang, Hao Gao +8
Current tomographic imaging systems need major improvements, especially when multi-dimensional, multi-scale, multi-temporal and multi-parametric phenomena are under investigation.…