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.
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Acknowledgement
We thank Pauline Guillemin from the University of Geneva, Switzerland, for her indispensable support with the data acquisition. This work was supported by the Swiss National Science Foundation, SNSF (320030_163330/1) and the GPU Grant Program of NVIDIA (Nvidia Corporation, Santa Clara, California, USA).
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Giger, A. et al. (2019). Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study. In: Rekik, I., Adeli, E., Park, S. (eds) Predictive Intelligence in Medicine. PRIME 2019. Lecture Notes in Computer Science(), vol 11843. Springer, Cham. https://doi.org/10.1007/978-3-030-32281-6_2
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DOI: https://doi.org/10.1007/978-3-030-32281-6_2
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