Rapid unpaired CBCT-based synthetic CT for CBCT-guided adaptive radiotherapy.
adaptive radiotherapy
deep learning
image synthesis
image translation
machine learning
Journal
Journal of applied clinical medical physics
ISSN: 1526-9914
Titre abrégé: J Appl Clin Med Phys
Pays: United States
ID NLM: 101089176
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
revised:
18
04
2023
received:
30
01
2023
accepted:
15
05
2023
medline:
11
10
2023
pubmed:
22
6
2023
entrez:
22
6
2023
Statut:
ppublish
Résumé
In this work, we demonstrate a method for rapid synthesis of high-quality CT images from unpaired, low-quality CBCT images, permitting CBCT-based adaptive radiotherapy. We adapt contrastive unpaired translation (CUT) to be used with medical images and evaluate the results on an institutional pelvic CT dataset. We compare the method against cycleGAN using mean absolute error, structural similarity index, root mean squared error, and Frèchet Inception Distance and show that CUT significantly outperforms cycleGAN while requiring less time and fewer resources. The investigated method improves the feasibility of online adaptive radiotherapy over the present state-of-the-art.
Identifiants
pubmed: 37345557
doi: 10.1002/acm2.14064
pmc: PMC10562022
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e14064Subventions
Organisme : NCI NIH HHS
ID : R01 CA215718
Pays : United States
Organisme : NIH HHS
ID : R01CA215718
Pays : United States
Organisme : NIH HHS
ID : R01EB032680
Pays : United States
Organisme : NIH HHS
ID : R56EB033332
Pays : United States
Organisme : NIH HHS
ID : P30 CA008748
Pays : United States
Informations de copyright
© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.
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