Towards real-time EPID-based 3D in vivo dosimetry for IMRT with Deep Neural Networks: A feasibility study.
Deep Neural Networks
EPID
In vivo dosimetry
Monte Carlo
Radiotherapy
Journal
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
ISSN: 1724-191X
Titre abrégé: Phys Med
Pays: Italy
ID NLM: 9302888
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
12
03
2023
revised:
17
08
2023
accepted:
22
09
2023
medline:
3
11
2023
pubmed:
7
10
2023
entrez:
6
10
2023
Statut:
ppublish
Résumé
We investigate the potential of the Deep Dose Estimate (DDE) neural network to predict 3D dose distributions inside patients with Monte Carlo (MC) accuracy, based on transmitted EPID signals and patient CTs. The network was trained using as input patient CTs and first-order dose approximations (FOD). Accurate dose distributions (ADD) simulated with MC were given as training targets. 83 pelvic CTs were used to simulate ADDs and respective EPID signals for subfields of prostate IMRT plans (gantry at 0
Identifiants
pubmed: 37801811
pii: S1120-1797(23)00175-8
doi: 10.1016/j.ejmp.2023.103148
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
103148Informations de copyright
Copyright © 2023. Published by Elsevier Ltd.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors have no relevant conflicts of interest to disclose.