An efficient dual-domain deep learning network for sparse-view CT reconstruction
This work presents an efficient dual-domain learning network for sparse-view CT reconstruction on raw projection data from a commercial scanner. The study also provides insights for designing an organ-based image quality assessment pipeline for sparse-view reconstruction tasks, potentially benefiting organ-specific dose reduction by sparse-view imaging.
Authors
- Yazdan Salimi (ORCID: https://orcid.org/0000-0002-1233-9576)
- Habib Zaidi (ORCID: https://orcid.org/0000-0001-7559-5297)
- Sana Boudabbous (ORCID: https://orcid.org/0000-0003-0516-4613)
- Chang Sun (ORCID: https://orcid.org/0000-0002-7039-889X)
- Neroladaki Angeliki
Institutions
- Obuda University (HU)
- University Medical Center Groningen (NL)
- Beijing University of Posts and Telecommunications (CN)
- University of Groningen (NL)
- University of Southern Denmark (DK)
- University Hospital of Geneva (CH)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2024-08-16
- DOI
- https://doi.org/10.1016/j.cmpb.2024.108376
- Citations
- 6
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.43
Funders
- Beijing University of Posts and Telecommunications
- H2020 Euratom