Validation and comparison of a biophysical modeling approach and non-linear registration for estimation of lung motion fields in thoracic 4D CT data

Rene Werner; Jan Ehrhardt; Alexander Schmidt-Richberg; Heinz Handels

doi:10.1117/12.811130

27 March 2009 Validation and comparison of a biophysical modeling approach and non-linear registration for estimation of lung motion fields in thoracic 4D CT data

Rene Werner, Jan Ehrhardt, Alexander Schmidt-Richberg, Heinz Handels

Author Affiliations +

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72590U (2009) https://doi.org/10.1117/12.811130
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Spatiotemporal image data allow analyzing respiratory dynamics and its impact on radiation therapy. A key feature within this field of research is the process of lung motion field estimation. For a multitude of applications feasible and "realistic" motion field estimates are required. Widely non-linear registration methods are applied to estimate motion fields; in this case physiology is not taken into account. Using Finite Element Methods we implemented a biophysical approach to model respiratory lung motion starting with the physiology of breathing. Resulting motion models are compared to motion field estimates of a non-linear non-parametric intensity-based registration approach. Additionally, we extended the registration approach to cope with discontinuities in pleura and chest wall motion as motivated by the biophysical model. Accuracy of the different modeling approaches is evaluated using a total of 800 user-defined landmarks in 4D(=3D+t) CT data of 10 lung tumor patients (between 70 and 90 landmarks each patient). Mean registration residuals (= difference between landmark motion as predicted model-based and as observed by an expert) are 3.2±2.0 mm (biophysical model), 3.4±2.4 mm (registration of segmented lung data), 2.1±2.3 mm (registration of CT data), and 1.6±1.3 mm (extended registration of CT data); intraobserver variability of landmark identification is 0.9±0.8 mm, mean landmark motion is 6.8±5.4 mm. Thus, prediction accuracy is higher for non-linear registration of the CT data, but it is shown that explicit modeling of boundary conditions motivated by the physiology of breathing and the biophysical modeling approach, respectively, improves registration accuracy significantly.

Citation Download Citation

Rene Werner, Jan Ehrhardt, Alexander Schmidt-Richberg, and Heinz Handels "Validation and comparison of a biophysical modeling approach and non-linear registration for estimation of lung motion fields in thoracic 4D CT data", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72590U (27 March 2009); https://doi.org/10.1117/12.811130

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