
Proceedings Paper
Population based modeling of respiratory lung motion and prediction from partial informationFormat | Member Price | Non-Member Price |
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Paper Abstract
Treatment of tumor sites affected by respiratory motion requires knowledge of the position and the shape of
the tumor and the surrounding organs during breathing. As not all structures of interest can be observed in
real-time, their position needs to be predicted from partial information (so-called surrogates) like motion of
diaphragm, internal markers or patients surface. Here, we present an approach to model respiratory lung motion
and predict the position and shape of the lungs from surrogates. 4D-MRI lung data of 10 healthy subjects was
acquired and used to create a model based on Principal Component Analysis (PCA). The mean RMS motion
ranged from 1.88 mm to 9.66 mm. Prediction was done using a Bayesian approach and an average RMSE of
1.44 mm was achieved.
Paper Details
Date Published: 13 March 2013
PDF: 7 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690U (13 March 2013); doi: 10.1117/12.2007076
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
PDF: 7 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690U (13 March 2013); doi: 10.1117/12.2007076
Show Author Affiliations
Dirk Boye, Paul Scherrer Institut (Switzerland)
ETH Zurich (Switzerland)
Golnoosh Samei, ETH Zurich (Switzerland)
Johannes Schmidt, Univ. of Zurich (Switzerland)
ETH Zurich (Switzerland)
ETH Zurich (Switzerland)
Golnoosh Samei, ETH Zurich (Switzerland)
Johannes Schmidt, Univ. of Zurich (Switzerland)
ETH Zurich (Switzerland)
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
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