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...

Полное описание

Библиографические подробности

Главные авторы:	Andreas Renner, Ingo Gulyas, Martin Buschmann, Gerd Heilemann, Barbara Knäusl, Martin Heilmann, Joachim Widder, Dietmar Georg, Petra Trnková
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Elsevier 2024-04-01
Серии:	Physics and Imaging in Radiation Oncology
Предметы:	Real-time tumour motion monitoring Motion prediction Intrafractional motion 4D image guidance Long short-term memory network
Online-ссылка:	http://www.sciencedirect.com/science/article/pii/S2405631624000642
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