Conditioning generative latent optimization for sparse-view computed tomography image reconstruction
We propose a parsimonious and robust reconstruction technique offering similar to better performances when compared with state-of-the-art methods regarding full-dose sparse-view CT. Our strategy could be readily applied to any imaging reconstruction task without any assumption about the acquisition protocol or the quantity of available data.
Authors
- Kévin Ginsburger (ORCID: https://orcid.org/0000-0002-8170-9964)
- V. Brandon
- David Hateau
- Thomas Braure
- Delphine Lazaro
Institutions
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université Paris-Saclay (FR)
- Laboratoire d'Intégration des Systèmes et des Technologies (FR)
- CEA DAM Île-de-France (FR)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1117/1.jmi.12.2.024004
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00