Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction-Reference-Cited by-同舟云学术

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Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction

Published:2019-03 Issue:2 Volume:3 Page:109-119
ISSN:2469-7311
Container-title:IEEE Transactions on Radiation and Plasma Medical Sciences
language:
Short-container-title:IEEE Trans. Radiat. Plasma Med. Sci.

Author:

Lee Hoyeon^ORCID,Lee Jongha^ORCID,Kim Hyeongseok^ORCID,Cho Byungchul^ORCID,Cho Seungryong^ORCID

Funder

National Research Foundation of Korea

Korea Evaluation Institute of Industrial Technology

KAIST

NST

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/ielx7/7433213/8657419/08452958.pdf?arnumber=8452958

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2. Enhanced 3D reconstruction of extreme sparse view terahertz computed tomography by using ASE-UNet incorporating asymmetric convolution blocks and channel attention mechanisms;Optics and Lasers in Engineering;2024-11

3. DdeNet: A dual-domain end-to-end network combining Pale-Transformer and Laplacian convolution for sparse view CT reconstruction;Biomedical Signal Processing and Control;2024-10

4. Artifact suppression for sparse view CT via transformer-based generative adversarial network;Biomedical Signal Processing and Control;2024-09

5. Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction;Journal of Scientific Computing;2024-08-20

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