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Population based modeling of respiratory lung motion and prediction from partial information

D. Boye, G. Samei, J. Schmidt, G. Székely, C. Tanner
SPIE Medical Imaging
2013

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.


Link to publisher's page
@InProceedings{eth_biwi_01017,
  author = {D. Boye and G. Samei and J. Schmidt and G. Székely and C. Tanner},
  title = {Population based modeling of respiratory lung motion and prediction from partial information},
  booktitle = {SPIE Medical Imaging},
  year = {2013},
  pages = {86690U},
  keywords = {}
}