67 citations · 279 across the 19 of their papers we have counts for
19 papers
Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning
Lequan Yu, Zhicheng Zhang, Xiaomeng Li +3
The presence of metallic implants often introduces severe metal artifacts in the X-ray CT images, which could adversely influence clinical diagnosis or dose calculation in radiatio…
A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction
Liyue Shen, Wei Zhao, Dante Capaldi +2
Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design and implementation have potential flaws. Fundamentally, most deep…
TransCT: Dual-path Transformer for Low Dose Computed Tomography
Zhicheng Zhang, Lequan Yu, Xiaokun Liang +2
Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radia…
Noise2Context: Context-assisted Learning 3D Thin-layer Low Dose CT Without Clean Data
Zhicheng Zhang, Xiaokun Liang, Wei Zhao +1
Computed tomography (CT) has played a vital role in medical diagnosis, assessment, and therapy planning, etc. In clinical practice, concerns about the increase of X-ray radiation e…
Dual-energy Computed Tomography Imaging from Contrast-enhanced Single-energy Computed Tomography
Wei Zhao, Tianling Lyu, Yang Chen +1
In a standard computed tomography (CT) image, pixels having the same Hounsfield Units (HU) can correspond to different materials and it is therefore challenging to differentiate an…
Beam data modeling of linear accelerators (linacs) through machine learning and its potential applications in fast and robust linac commissioning and quality assurance
Wei Zhao, Ishan Patil, Bin Han +3
Background and purpose: To propose a novel machine learning-based method for reliable and accurate modeling of linac beam data applicable to the processes of linac commissioning an…
Dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network
Tianling Lyu, Zhan Wu, Yikun Zhang +3
Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are…
A deep learning approach for virtual monochromatic spectral CT imaging with a standard single energy CT scanner
Wei Zhao, Tianling Lyu, Yang Chen +1
Purpose/Objectives: To develop and assess a strategy of using deep learning (DL) to generate virtual monochromatic CT (VMCT) images from a single-energy CT (SECT) scan. Materials/M…
Automatic target positioning and tracking for image-guided radiotherapy without implanted fiducials
Wei Zhao, Liyue Shen, Yan Wu +3
Current image-guided prostate radiotherapy often relies on the use of implanted fiducials or transducers for target localization. Fiducial or transducer insertion requires an invas…
Dual-energy CT imaging using a single-energy CT data is feasible via deep learning
Wei Zhao, Tianling Lv, Peng Gao +6
In a standard computed tomography (CT) image, pixels having the same Hounsfield Units (HU) can correspond to different materials and it is, therefore, challenging to differentiate…
Multi-materials beam hardening artifacts correction for computed tomography (CT) based on X-ray spectrum estimation
Wei Zhao, Dengwang Li, Kai Niu +3
Due to the energy-dependent nature of the attenuation coefficient and the polychromaticity of the X-ray source, beam hardening effect occurs when X-ray photons penetrate through an…
A unified image reconstruction framework for quantitative dual- and triple-energy CT imaging of material compositions
Wei Zhao, Don Vernekohl, Fei Han +4
Many clinical applications depend critically on the accurate differentiation and classification of different types of materials in patient anatomy. This work introduces a unified f…
Segmentation-Free X-ray Energy Spectrum Estimation for Computed Tomography Using Dual-Energy Material Decomposition
Wei Zhao, Lei Xing, Qiude Zhang +2
X-ray energy spectrum plays an essential role in computed tomography (CT) imaging and related tasks. Due to the high photon flux of clinical CT scanners, most of spectrum estimatio…
Segmentation-free x-ray energy spectrum estimation for computed tomography
Wei Zhao, Qiude Zhang, Tianye Niu
X-ray energy spectrum plays an essential role in imaging and related tasks. Due to the high photon flux of clinical CT scanners, most of the spectrum estimation methods are indirec…
A Model-Based Scatter Artifacts Correction for Cone Beam CT
Wei Zhao, Don Vernekohl, Jun Zhu +2
The purpose of this work is to provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging. The method starts with an estimation of coarse sc…
Using Edge-Preserving Algorithm with Non-local Mean for Significantly Improved Image-Domain Material Decomposition in Dual Energy CT
Wei Zhao, Tianye Niu, Lei Xing +6
Increased noise is a general concern for dual-energy material decomposition. Here, we develop an image-domain material decomposition algorithm for dual-energy CT (DECT) by incorpor…
An indirect transmission measurement-based spectrum estimation method for computed tomography
Wei Zhao, Kai Niu, Sebastian Schafer +1
The characteristics of an x-ray spectrum can greatly influence imaging and related tasks. In practice, due to the pile-up effect of the detector, it's difficult to directly measure…
Fast Scatter Artifacts Correction for Cone-Beam CT without System Modification and Repeat Scan
Wei Zhao, Jun Zhu, Luyao Wang
We provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging. The method starts with an estimation of coarse scatter profile for a set of C…
A patient-specific scatter artifacts correction method
Wei Zhao, Stephen Brunner, Kai Niu +3
This paper provides a fast and patient-specific scatter artifact correction method for cone-beam computed tomography (CBCT) used in image-guided interventional procedures. Due to i…