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Proceedings Article

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

[+] Author Affiliations
Dirk Boye

Paul Scherrer Institut (Switzerland)

Golnoosh Samei, Gabor Székely, Christine Tanner

ETH Zurich (Switzerland)

Johannes Schmidt

Univ. of Zurich (Switzerland)

Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690U (March 13, 2013); doi:10.1117/12.2007076
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From Conference Volume 8669

  • Medical Imaging 2013: Image Processing
  • Sebastien Ourselin; David R. Haynor
  • Lake Buena Vista (Orlando Area), Florida, USA | February 09, 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. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

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


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