Optimizing external surface sensor locations for respiratory tumor motion prediction

Abstract

Real-time tracking of tumor motion due to the patient’s respiratory cycle is a crucial task in radiotherapy treatments. In this work a proof-of-concept setup is presented where real-time tracked external skin attached sensors are used to predict the internal tumor locations. The spatiotemporal relationships between external sensors and targets during the respiratory cycle are modeled using Gaussian Process regression and trained on a preoperative 4D-CT image sequence of the respiratory cycle. A large set (N≈ 25) of computer-tomography markers are attached on the patient’s skin before CT acquisition to serve as candidate sensor locations from which a smaller subset (N≈ 6) is selected based on their combined predictive power using a genetic algorithm based optimization technique. A custom 3D printed sensor-holder design is used to allow accurate positioning of optical or electromagnetic sensors at the best predictive CT marker locations preoperatively, which are then used for real-time prediction of the internal tumor locations. The method is validated on an artificial respiratory phantom model. The model represents the candidate external locations (fiducials) and internal targets (tumors) with CT markers. A 4D-CT image sequence with 11 time-steps at different phases of the respiratory cycles was acquired. Within this test setup, the CT markers for both internal and external structures are automatically determined by a morphology-based algorithm in the CT images. The method’s in-sample cross validation accuracy in the training set as given by the average root mean-squared error (RMSE) is between 0.00024 and 0.072 mm.