Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning
Published inEuropean journal of nuclear medicine and molecular imaging, vol. 50, no. 4, p. 1034-1050
Publication date2023-03
First online date2022-12-12
Abstract
Keywords
- Attenuation correction
- Deep learning
- Distributed learning
- Federated learning
- PET
- Deep Learning
- Humans
- Image Processing, Computer-Assisted / methods
- Magnetic Resonance Imaging / methods
- Positron Emission Tomography Computed Tomography
- Positron-Emission Tomography / methods
Affiliation entities
- Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale
- Faculté des sciences / Département d'informatique
- Faculté de médecine / Section de médecine clinique / Département des neurosciences cliniques
- Centres et instituts / Centre interfacultaire de neurosciences
- Faculté des sciences / Section de physique / Département de physique théorique
Funding
- Swiss National Science Foundation - Towards patient-specific hybrid whole-body PET parametric imaging [176052]
- European Commission - Eurostars program [grant E! 114021 ProVision]
- Private Foundation of Geneva University Hospitals - [RC-06-01]
Citation (ISO format)
SHIRI LORD, Isaac et al. Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning. In: European journal of nuclear medicine and molecular imaging, 2023, vol. 50, n° 4, p. 1034–1050. doi: 10.1007/s00259-022-06053-8
Main files (1)
Article (Published version)
Secondary files (1)
Identifiers
- PID : unige:166947
- DOI : 10.1007/s00259-022-06053-8
- PMID : 36508026
- PMCID : PMC9742659
Commercial URLhttps://link.springer.com/10.1007/s00259-022-06053-8
ISSN of the journal1619-7070