4 citations · 9 across the 3 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…