Real-Time 4-D-Cone Beam CT Accurate Estimation Based on Single-Angle Projection via Dual-Attention Mechanism Residual Network-Reference-Cited by-同舟云学术

Real-Time 4-D-Cone Beam CT Accurate Estimation Based on Single-Angle Projection via Dual-Attention Mechanism Residual Network

Published:2023-07 Issue:6 Volume:7 Page:618-629
ISSN:2469-7311
Container-title:IEEE Transactions on Radiation and Plasma Medical Sciences
language:
Short-container-title:IEEE Trans. Radiat. Plasma Med. Sci.

Author:

Xiao Hanguang¹^ORCID,Chen Kai¹,You Tao²,Liu Daidai¹,Zhang Wei¹^ORCID,Xue Xufeng¹,Long Li¹,Dang Jun³^ORCID

Affiliation:

1. School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China

2. Department of Radiation Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang, China

3. Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Chongqing

Shenzhen Science and Technology Program

Postgraduate Research Innovation Project of Chongqing

Basic and Applied Basic Research Foundation of Guangdong Province

General Project of the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

Jiangsu Provincial Health Commission Medical Research Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Radiology, Nuclear Medicine and imaging,Instrumentation,Atomic and Molecular Physics, and Optics

Link

http://xplorestaging.ieee.org/ielx7/7433213/10169864/10077783.pdf?arnumber=10077783

Reference33 articles.

1. Real-time tumor localization with single x-ray projection at arbitrary gantry angles using a convolutional neural network (CNN)

2. Convolutional Neural Network (CNN) Based Three Dimensional Tumor Localization Using Single X-Ray Projection

3. Deep Learning in Radiology: Now the Real Work Begins

4. An unsupervised 2D–3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation

5. SAUNet: An automatic segmentation model of COVID-19 lesion from CT slices;xiao;Vis Comput,2022