LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT-Reference-Cited by-同舟云学术

LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT

Published:2023-02 Issue:2 Volume:7 Page:132-142
ISSN:2469-7311
Container-title:IEEE Transactions on Radiation and Plasma Medical Sciences
language:
Short-container-title:IEEE Trans. Radiat. Plasma Med. Sci.

Author:

Zhang Yi¹^ORCID,Chen Hu²^ORCID,Xia Wenjun²^ORCID,Chen Yang³^ORCID,Liu Baodong⁴^ORCID,Liu Yan⁵^ORCID,Sun Huaiqiang⁶^ORCID,Zhou Jiliu²

Affiliation:

1. School of Cyber Science and Engineering, Sichuan University, Chengdu, China

2. College of Computer Science, Sichuan University, Chengdu, China

3. Laboratory of Image Science and Technology, Southeast University, Nanjing, China

4. Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China

5. School of Electrical Engineering Information, Sichuan University, Chengdu, China

6. Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

Funder

National Natural Science Foundation of China

Sichuan Science and Technology Program

Sichuan University “From 0 to 1” Innovative Research Program

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/ielx7/7433213/10035778/09951388.pdf?arnumber=9951388

Reference61 articles.

1. Adam: A method for stochastic optimization;kingma;arXiv 1412 6980,2014

2. Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network

3. 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network

4. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction

5. Understanding the difficulty of training deep feedforward neural networks;glorot;Proc Int Conf Artif Intell Stat (AISTATS),2010

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