Self-Training Strategy Based on Finite Element Method for Adaptive Bioluminescence Tomography Reconstruction-Reference-Cited by-同舟云学术

Self-Training Strategy Based on Finite Element Method for Adaptive Bioluminescence Tomography Reconstruction

Published:2022-10 Issue:10 Volume:41 Page:2629-2643
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Zhang Xuanxuan¹^ORCID,Cao Xu²,Zhang Peng³^ORCID,Song Fan³,Zhang Jiulou⁴,Zhang Lin⁵,Zhang Guanglei³^ORCID

Affiliation:

1. School of Communications and Information Engineering and the School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Shaanxi, Xi’an, China

2. Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Shaanxi, Xi’an, China

3. Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China

4. School of Medical Imaging, Nanjing Medical University, Jiangsu, Nanjing, China

5. School of Information Science and Engineering, Shandong Normal University, Shandong, Jinan, China

Funder

National Natural Science Foundation of China

Shaanxi Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/9906463/09758813.pdf?arnumber=9758813

Reference60 articles.

1. Learning-Based Compressive MRI

2. DIMENSION: Dynamic MR imaging with both k‐space and spatial prior knowledge obtained via multi‐supervised network training

3. Radon Inversion via Deep Learning

4. Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems

5. Image prediction for limited-angle tomography via deep learning with convolutional neural network;zhang;arXiv 1607 08707,2016

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Dual and Multi-Target Cone-Beam X-ray Luminescence Computed Tomography Based on the DeepCB-XLCT Network;Bioengineering;2024-08-28

2. Highly robust reconstruction framework for three-dimensional optical imaging based on physical model constrained neural networks;Physics in Medicine & Biology;2024-03-21

3. Multi-target reconstruction based on subspace decision optimization for bioluminescence tomography;Computer Methods and Programs in Biomedicine;2023-10

4. 3D-deep optical learning: a multimodal and multitask reconstruction framework for optical molecular tomography;Optics Express;2023-07-03

5. Two-stage deep learning method for sparse-view fluorescence molecular tomography reconstruction;Journal of the Optical Society of America A;2023-06-20