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Proceedings Paper

Population based modeling of respiratory lung motion and prediction from partial information
Author(s): Dirk Boye; Golnoosh Samei; Johannes Schmidt; Gabor Székely; Christine Tanner
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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
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)
Gabor Székely, ETH Zurich (Switzerland)
Christine Tanner, 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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