At external beam radiotherapy, stereoscopic X-ray imaging system is responsible as tumor motion information provider. This system takes X-ray images intermittently from tumor position (1) at pretreatment step to provide training data set for model construction and (2) during treatment to control the accuracy of correlation model performance. In this work, we investigated the effect of imaging data points provided by this system on treatment quality. Because some information is still lacking about (1) the number of imaging data points, (2) shooting time for capturing each data point, and also (3) additional imaging dose delivered by this system. These 3 issues were comprehensively assessed at (1) pretreatment step while training data set is gathered for prediction model construction and (2) during treatment while model is tested and reconstructed using new arrival data points. A group of real patients treated with CyberKnife Synchrony module was chosen in this work, and an adaptive neuro-fuzzy inference system was considered as consistent correlation model. Results show that a proper model can be constructed while the number of imaging data points is highly enough to represent a good pattern of breathing cycles. Moreover, a trade-off between the number of imaging data points and additional imaging dose is considered in this study. Since breathing phenomena are highly variable at different patients, the time for taking some of imaging data points is very important, while their absence at that critical time may yield wrong tumor tracking. In contrast, the sensitivity of another category of imaging data points is not high, while breathing is normal and in the control range. Therefore, an adaptive supervision on the implementation of stereoscopic X-ray imaging is proposed to intelligently accomplish shooting process, based on breathing motion variations.
A Study on Stereoscopic X-ray Imaging Data Set on the Accuracy of Real-Time Tumor Tracking in External Beam Radiotherapy
