A review of artificial intelligence applications for motion tracking in radiotherapy.
artificial intelligence
deep learning
machine learning
motion tracking
radiation oncology
Journal
Journal of medical imaging and radiation oncology
ISSN: 1754-9485
Titre abrégé: J Med Imaging Radiat Oncol
Pays: Australia
ID NLM: 101469340
Informations de publication
Date de publication:
Aug 2021
Aug 2021
Historique:
received:
05
03
2021
accepted:
29
06
2021
pubmed:
22
7
2021
medline:
25
2
2023
entrez:
21
7
2021
Statut:
ppublish
Résumé
During radiotherapy, the organs and tumour move as a result of the dynamic nature of the body; this is known as intrafraction motion. Intrafraction motion can result in tumour underdose and healthy tissue overdose, thereby reducing the effectiveness of the treatment while increasing toxicity to the patients. There is a growing appreciation of intrafraction target motion management by the radiation oncology community. Real-time image-guided radiation therapy (IGRT) can track the target and account for the motion, improving the radiation dose to the tumour and reducing the dose to healthy tissue. Recently, artificial intelligence (AI)-based approaches have been applied to motion management and have shown great potential. In this review, four main categories of motion management using AI are summarised: marker-based tracking, markerless tracking, full anatomy monitoring and motion prediction. Marker-based and markerless tracking approaches focus on tracking the individual target throughout the treatment. Full anatomy algorithms monitor for intrafraction changes in the full anatomy within the field of view. Motion prediction algorithms can be used to account for the latencies due to the time for the system to localise, process and act.
Identifiants
pubmed: 34288501
doi: 10.1111/1754-9485.13285
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
596-611Subventions
Organisme : Cancer Institute NSW
ID : Early Career Fellowship 2018-ECF007
Organisme : National Health and Medical Research Council
ID : Early Career Fellowship GNT1138807
Informations de copyright
© 2021 The Royal Australian and New Zealand College of Radiologists.
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