Tumor motion induced by patient breathing decreases the effectiveness of radiation treatment. Image guided
radiation treatment (IGRT) is an advanced approach for cancer radiation treatment. The success of IGRT is
largely dependent on the accurate localization of tumor in real-time. There are two major imaging approaches
currently in use to localize a tumor: internal imaging and external imaging. Internal imaging determines the
tumor locations by directly x-ray of the tumor area. It is accurate however radiation dose is a big concern.
External imaging derives the internal tumor locations through an external mark on the patient surface. It is
radiation dose free however the insufficient accuracy limits its wide application. Integrating the internal and
external signals together is necessary for reliable radiation treatment and acceptable patient radiation exposure.
Our work tries to identify the correlation patterns between internal/external signals and the influential factors
so that the hybrid signal will give desire accuracy in dose delivery while limiting radiation exposure to the
patients. Both theoretical simulation based on sinusoidal functions and statistical analysis on real patient data
are performed. The sinusoidal simulation will identify the potential influence factors of different correlation
conditions. The results have demonstrated the various correlation patterns with amplitude various, frequency
changes (duration changes), phase shifts, and baseline drift. The results will aid the statistical analytical on
real-patients to identify the dominant factors of the internal/external motion signals for a specific patients. The
described work is very useful in advanced IGRT to update the internal/external correlation in real-time for better
cancer patient care.