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

Dirk Boye; Golnoosh Samei; Johannes Schmidt; Gabor Székely; Christine Tanner

doi:10.1117/12.2007076

13 March 2013 Population based modeling of respiratory lung motion and prediction from partial information

Dirk Boye, Golnoosh Samei, Johannes Schmidt, Gabor Székely, Christine Tanner

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86690U (2013) https://doi.org/10.1117/12.2007076
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download 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 (13 March 2013); https://doi.org/10.1117/12.2007076

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available