Tracking of Tumor Motion in Lung Cancer using Patient Specific Finite Element Modeling and 4D-MRI Image Data

Soo-Kng Teo, Yuxin Yang, Eric Van Reeth, Shuai He, Peijun Chua, and Chueh Loo Poh


Tumor motion, Lung cancer, Finite element modeling, 4D-MRI


This paper presents a study that demonstrates the potential of using finite element (FE) lung model constructed using 4D-MRI (3D + time) for tracking tumor motion during a respiratory cycle. A series of volumetric images of one lung cancer patient was acquired over time under free breathing and sorted into respiratory phases. A FE model of the lung with the tumor was constructed using the volume which is at full exhale phase. Displacement field from this initial volume to the subsequent 3D volumes in the respiratory phases were derived using a deformable image registration technique. This displacement field which provides displacement information of the lung surface is then used to predict the tumor motion in the lung interior using the FE model. Our results showed that the tumor motion (as represented by the trajectory of the tumor centroid) follows a highly non-linear path during the respiratory cycle from the full exhale phase to the full inhale phase. We also showed that the predicted tumor motion from our FE model is in reasonable agreement with that computed from 4D-MRI.

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