DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction-Reference-Cited by-同舟云学术

DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction

Published:2022-12 Issue:12 Volume:41 Page:3587-3599
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Zhou Bo¹^ORCID,Chen Xiongchao¹^ORCID,Xie Huidong¹^ORCID,Zhou S. Kevin²^ORCID,Duncan James S.³^ORCID,Liu Chi³^ORCID

Affiliation:

1. Department of Biomedical Engineering, Yale University, New Haven, CT, USA

2. School of Biomedical Engineering and the Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China

3. Department of Biomedical Engineering and the Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA

Funder

National Institutes of Health

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/ielx7/42/9969446/09825711.pdf?arnumber=9825711

Reference59 articles.

1. Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography

2. DuDoNet: Dual Domain Network for CT Metal Artifact Reduction

3. Deep learning methods to guide CT image reconstruction and reduce metal artifacts;gjesteby;Proc SPIE,2017

4. U-Net: Convolutional networks for biomedical image segmentation;ronneberger;Proc Int Conf Med Image Comput Comput -Assist Intervent,2015

5. CT sinogram‐consistency learning for metal‐induced beam hardening correction

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