Towards modeling tumor motion in the deflated lung for minimally invasive ablative procedures.

A computational model is proposed to demonstrate the feasibility of characterizing the motion of lung tumors caused by respiratory diaphragm forces using a tissue biomechanics approach. Compensating for such motion is very important for developing effective systems of minimally invasive tumor ablative procedures, e.g., Low Dose Rate (LDR) lung brachytherapy. To minimize the effects of respiratory motion, the target lung is almost completely deflated before starting such procedures. However, a significant amount of motion persists in the target lung due to the diaphragm contact forces required for the other lung's respiration. In this study, a model pipeline was developed which inputs a pre-operative 4D-CT image sequence of the lung to output the predicted 3D motion trajectory of the tumor over the respiratory cycle. A finite element method was used in this pipeline to model the lung tissue deformation in order to predict the tumor motion. Experiments were conducted on an ex vivo porcine lung to demonstrate the performance and assess the accuracy of the proposed pipeline. The resultant tumor motion trajectory obtained from the biomechanical model of the lung was compared to the experimental trajectory obtained from CT imaging. Results were promising, suggesting that tissue mechanics-based modeling can be employed for effective characterization of lung tumor respiratory motion to improve accuracy in lung tumor ablative procedures.