SU‐E‐J‐163: A Biomechanical Lung Model for Respiratory Motion Study

Abstract

Purpose: This work presents a biomechanical model to investigate the complex respiratory motion for the lung tumor tracking in radiosurgery by computer simulation. Methods: The models include networked massspring‐dampers to describe the tumor motion, different types of surrogate signals, and the force generated by the diaphragm. Each mass‐springdamper has the same mechanical structure and each model can have different numbers of mass‐spring‐dampers. Both linear and nonlinear stiffness parameters were considered, and the damping ratio was tuned in a range so that the tumor motion was over‐damped (no natural tumor oscillation occurs without force from the diaphragm). The simulation was run by using ODE45 (ordinary differential equations by Runge‐Kutta method) in MATLAB, and all time courses of motions and inputs (force) were generated and compared. Results: The curvature of the motion time courses around their peaks was sensitive to the damping ratio. Therefore, the damping ratio can be determined based on the clinical data of a high sampling rate. The peak values of different signals and the time the peaks occurred were compared, and it was found that the diaphragm force had a time lead over the tumor motion, and the lead time (0.1–0.4 seconds) depended on the distance between the tumor and the diaphragm. Conclusion: We reported a model based analysis approach for the spatial and temporal relation between the motion of the lung tumor and the surrogate signals. Due to the phase lead of the diaphragm in comparing with the lung tumor motion, the measurement of diaphragm motion (or its electromyography signal) can be used as a beam gating signal in radiosurgery, and it can also be an additional surrogate signal for better tumor motion tracking. The research is funded by the American Cancer Society (ACS) grant. The grant name is: Frameless SRS Based on Robotic Head Motion Cancellation. The grant number is: RSG‐13‐313‐01‐CCE