Direct Filter Learning From Iterative Reconstructed Images for High-Quality Analytical CBCT Reconstruction Using FDK-Based Neural Network-Reference-Cited by-同舟云学术

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Direct Filter Learning From Iterative Reconstructed Images for High-Quality Analytical CBCT Reconstruction Using FDK-Based Neural Network

Published:2024 Issue: Volume:12 Page:121495-121506
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Qi Hongliang¹^ORCID,Long Chao²,Li Hanwei¹,Huang Shuang³,Hu Debin¹,Xu Yuan⁴^ORCID,Chen Hongwen¹

Affiliation:

1. Department of Clinical Engineering, Nanfang Hospital, Southern Medical University, Guangzhou, China

2. School of Graduate Studies, La Consolacion University Philippines, Malolos, Philippines

3. Foshan Shunde Heyou Hospital, Foshan, China

4. School of Biomedical Engineering, Southern Medical University, Guangzhou, China

Funder

National Key Research and Development Program of China

Key-Area Research and Development Program of Guangdong

President Foundation of Nanfang Hospital, Southern Medical University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10568921.pdf?arnumber=10568921

Reference40 articles.

1. Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT;Sidky;J. X-Ray Sci. Technol.,2009

2. Prior image constrained compressed sensing (PICCS)

3. TICMR: Total Image Constrained Material Reconstruction via Nonlocal Total Variation Regularization for Spectral CT

4. Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy

5. Practical cone-beam algorithm

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