Validation and Comparison of a Biophysical Modeling Approach and Non-Linear Registration for Estimation of Lung Motion Fields in Thoracic 4D CT Data

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.