Population-based 3D respiratory motion modelling from convolutional autoencoders for 2D ultrasound-guided radiotherapy

•Motion compensation model for free-breathing ultrasound-guided radiotherapy treatments.•A novel real-time motion modelling framework composed of a rigid alignment module and a deep deformable model.•Inference requires only two pre-treatment volumes and live 2D images representing the current state...

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Veröffentlicht in:	Medical image analysis Jg. 75; S. 102260
Hauptverfasser:	Mezheritsky, Tal, Romaguera, Liset Vázquez, Le, William, Kadoury, Samuel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.01.2022 Elsevier BV
Schlagworte:	Breathing Convolutional autoencoders Deep learning Deformable registration Formability Humans Imaging, Three-Dimensional Liver Liver cancer Modelling Motion Motion modelling Radiation therapy Radiotherapy, Image-Guided Real time Respiration Similarity Three dimensional models Three dimensional motion Tracking errors Two dimensional models Ultrasonic imaging Ultrasonography Ultrasonography, Interventional Ultrasound Ultrasound-guided radiotherapy United States > US Liver cancer Convolutional autoencoders 41A10 65D05 Motion modelling 65D17 Ultrasound-guided radiotherapy 41A05 Deformable registration
ISSN:	1361-8415, 1361-8423, 1361-8423
Online-Zugang:	Volltext
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