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Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study
ConferencePaper (Artikel, die in Tagungsbänden erschienen sind)
 
ID 4611881
Author(s) Giger, Alina; Jud, Christoph; Nguyen, Damien; Krieger, Miriam; Zhang, Ye; Lomax, Antony J.; Bieri, Oliver; Salomir, Rares; Cattin, Philippe C.
Author(s) at UniBasel Jud, Christoph
Nguyen, Damien
Bieri, Oliver
Cattin, Philippe Claude
Giger, Alina
Year 2019
Title Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study
Editor(s) Rekik, Islem; Adeli, Ehsan; Park, Sang Hyun
Book title (Conference Proceedings) Predictive Intelligence in Medicine
Volume 11843
Place of Conference Shenzhen, China
Year of Conference 2019
Publisher Springer
Place of Publication Cham
Pages 11-22
ISSN/ISBN 0302-9743 ; 1611-3349 ; 978-3-030-32280-9 ; 978-3-030-32281-6
Keywords Motion prediction, ultrasound, 4D MRI, radiotherapy
Abstract Motion management strategies are crucial for radiotherapy of mobile tumours in order to ensure proper target coverage, save organs at risk and prevent interplay effects. We present a feasibility study for an inter-fractional, patient-specific motion model targeted at active beam scanning proton therapy. The model is designed to predict dense lung motion information from 2D abdominal ultrasound images. In a pretreatment phase, simultaneous ultrasound and magnetic resonance imaging are used to build a regression model. During dose delivery, abdominal ultrasound imaging serves as a surrogate for lung motion prediction. We investigated the performance of the motion model on five volunteer datasets. In two cases, the ultrasound probe was replaced after the volunteer has stood up between two imaging sessions. The overall mean prediction error is 2.9 mm and 3.4 mm after repositioning and therefore within a clinically acceptable range. These results suggest that the ultrasound-based regression model is a promising approach for inter-fractional motion management in radiotherapy.
Series title Lecture Notes in Computer Science
edoc-URL https://edoc.unibas.ch/80471/
Full Text on edoc Available
Digital Object Identifier DOI 10.1007/978-3-030-32281-6_2
 
   

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