Author: Damien C. Mathew, Brandon Hai Nguyen, Satwik Pani, Yoichi Watanabe 👨🔬
Affiliation: Minnespolis Radiation Oncology, Department of Radiation Oncology, University of Minnesota Medical School, Washington University 🌍
Purpose: To formulate a computational algorithm to quantify the tumor movement from the motion data of landmarks on the brain surface. To estimate the magnitude of tumor movement when the head makes a translational shift and a rotation during a Gamma Knife radiosurgery (GKRS) by computer simulations.
Methods: First, we developed a computational algorithm using the barycentric coordinate system for four landmarks on the brain surface, Points A, B, C, and D, and one point P in the tumor. The method gave new coordinates of Point P by measuring the new coordinates of the four points when the head moved. Next, we simulated the head movement and estimated the motion of those points. The head was assumed to randomly move in three orthogonal directions and rotate around three axes. The magnitudes of translational shifts and rotation angles were sampled from normal distributions with clinically feasible standard deviations. The location of P was uniformly sampled in a volume enclosed by the brain. In particular, we simulated the motion of the nose tip to estimate the tumor movement during GKRS and study the relationship between the movement of tumors and the nose tip, whose motion was monitored by the High-Definition Motion Management (HDMM) system.
Results: A simulation using an actual patient geometry showed that the nose tip moved on average by 3.48 ± 1.677 mm, whereas the tumor movement was 1.93 ± 0.847 mm. The tumor displacement was smaller than the nose tip for most head movements and tumor locations.
Conclusion: The algorithm was formulated to estimate tumor motion using the spatial coordinates of four points on the brain surface. The computer simulations showed that the actual movement of the tumor was overestimated by using only the motion data of the nose tip.