Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data

Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data

Background and purpose: Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they...

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Bibliographic Details
Main Authors: Andreas Renner, Ingo Gulyas, Martin Buschmann, Gerd Heilemann, Barbara Knäusl, Martin Heilmann, Joachim Widder, Dietmar Georg, Petra Trnková
Format: Article
Language:English
Published: Elsevier 2024-04-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
Real-time tumour motion monitoring
Motion prediction
Intrafractional motion
4D image guidance
Long short-term memory network
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631624000642

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http://www.sciencedirect.com/science/article/pii/S2405631624000642

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