Sinogram + image domain neural network approach for metal artifact reduction in low-dose cone-beam computed tomography-Reference-Cited by-同舟云学术

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Sinogram + image domain neural network approach for metal artifact reduction in low-dose cone-beam computed tomography

Published:2021-03-13 Issue:05 Volume:8 Page:
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:
Short-container-title:J. Med. Imag.

Author:

Ketcha Michael D.¹,Marrama Michael²,Souza Andre²,Uneri Ali¹,Wu Pengwei¹,Zhang Xiaoxuan¹,Helm Patrick A.²,Siewerdsen Jeffrey H.¹

Affiliation:

1. Johns Hopkins University, Department of Biomedical Engineering, Baltimore Maryland

2. Medtronic, Littleton, Massachusetts

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology, Nuclear Medicine and imaging

Cited by 15 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. Artifact suppression for breast specimen imaging in micro CBCT using deep learning;BMC Medical Imaging;2024-02-06

2. Mud-Net: multi-domain deep unrolling network for simultaneous sparse-view and metal artifact reduction in computed tomography;Machine Learning: Science and Technology;2024-01-17

3. Simulation‐driven training of vision transformers enables metal artifact reduction of highly truncated CBCT scans;Medical Physics;2023-12-27

4. MARGANVAC: metal artifact reduction method based on generative adversarial network with variable constraints;Physics in Medicine & Biology;2023-10-02

5. Image quality improvement in bowtie‐filter‐equipped cone‐beam CT using a dual‐domain neural network;Medical Physics;2023-09-05

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