Prediction of Respiratory Motion with Wavelet-based Multiscale Autoregression

Abstract

In robotic radiosurgery, a photon beam source, moved by a robot arm, is used to ablate tumors. The accuracy of the treatment can be improved by predicting respiratory motion to compensate for system delay. We consider a wavelet-based multiscale autoregressive prediction method. The algorithm is extended by introducing a new exponential averaging parameter and the use of the Moore-Penrose pseudo inverse to cope with long-term signal dependencies and system matrix irregularity, respectively. In test cases, this new algorithm outperforms normalized LMS predictors by as much as 50%. With real patient data, we achieve an improvement of around 5 to 10%.

Original languageEnglish
Title of host publicationMICCAI 2007: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
Number of pages8
Volume4792
PublisherSpringer Verlag
Publication date01.12.2007
Pages668-675
ISBN (Print)978-3-540-75758-0
ISBN (Electronic)978-3-540-75759-7
DOIs
Publication statusPublished - 01.12.2007
Event10th International Conference on Medical Imaging and Computer-Assisted Intervention - Brisbane, Australia
Duration: 29.10.200702.11.2007
Conference number: 71013

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