On the Benefit of Dual-Domain Denoising in a Self-Supervised Low-Dose CT Setting-Reference-Cited by-同舟云学术

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On the Benefit of Dual-Domain Denoising in a Self-Supervised Low-Dose CT Setting

Published:2023-04-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
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Author:

Wagner Fabian¹,Thies Mareike¹,Pfaff Laura¹,Aust Oliver²,Pechmann Sabrina³,Weidner Daniela²,Maul Noah¹,Rohleder Maximilian¹,Gu Mingxuan¹,Utz Jonas⁴,Denzinger Felix¹,Maier Andreas¹

Affiliation:

1. FAU Erlangen-Nürnberg,Pattern Recognition Lab,Germany

2. FAU Erlangen-Nürnberg,Department of Rheumatology and Immunology,Germany

3. Fraunhofer Institute for Ceramic Technologies and Systems IKTS,Germany

4. FAU Erlangen-Nürnberg,Department AIBE,Germany

Funder

European Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230511.pdf?arnumber=10230511

Reference18 articles.

1. Technical Note: PYRO‐NN: Python reconstruction operators in neural networks

2. Next-generation imaging of the skeletal system and its blood supply

3. Single-slice rebinning method for helical cone-beam CT

4. Technical Note: Development and validation of an open data format for CT projection data

Cited by 7 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. Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies;Journal of Imaging Informatics in Medicine;2024-09-04

2. Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction;Physics in Medicine & Biology;2024-08-09

3. Self-supervised dual-domain balanced dropblock-network for low-dose CT denoising;Physics in Medicine & Biology;2024-03-26

4. A Dual-Domain Diffusion Model for Sparse-View CT Reconstruction;IEEE Signal Processing Letters;2024

5. Neural Network-based Sinogram Upsampling in Real-measured CT Reconstruction;Informatik aktuell;2024

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