Investigating Motion Data Selections Based on Patient-Specific Respiration Pattern at External Surrogates Radiotherapy Using Cyberknife Synchrony Respiratory Tracking System

Abstract

Introduction: : In order to personalize motion compensated radiotherapy with external surrogates, an intelligent method is proposed for selecting external surrogates' motion data on the basis of patient-specific respiration pattern. This strategy enhances targeting accuracy and can potentially feed the stereoscopic X-ray imaging system and lead to fewer imaging dose, intelligently. Material and Methods: We investigate the effects of training data points firstly on correlation model construction at pre-treatment step for its training. Then, the same assessment will be done by means of updating data points on the model re-construction. Moreover, a recognition algorithm has been developed to detect high variability of breathing motion using pre-defined discriminator levels based on external motion amplitude. Results: The number of training and updating data points can be intelligently optimized depending on the breathing pattern of each patient. In addition, by developing recognition algorithm, the shooting time for motion data selection is converted from conventional strategy to intelligent approach, accordingly. As example, for a patient with high motion variability while the number of critical data points recognized by our algorithm is significant, the targeting error with and without utilizing these data points are 4.4 mm and 6.6 mm, respectively. Conclusion: This work promises to be aid a more personalized delivery of motion compensated radiotherapy using external surrogates by considering to motion data gathering, according to patient-specific respiration pattern. By implementing our strategy, we expect to make a compromise between the performance accuracy of correlation model and additional imaging dose.