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.
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Dirk Boye ; Golnoosh Samei ; Johannes Schmidt ; Gabor Székely and Christine Tanner
Population based modeling of respiratory lung motion and prediction from partial information
", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690U (March 13, 2013); doi:10.1117/12.2007076; http://dx.doi.org/10.1117/12.2007076