Data Driven Cone Beam CT Motion Management for Radiotherapy Application

The ability to identify respiratory motion is crucial during radiation therapy treatment. In our study we introduced a novel data driven method based on principal component analysis (PCA) to extract a signal related to respiratory motion from cone beam CT projection data. Projection data acquired on cone beam CT devices normally has two motion component information within it, (1) respiratory induced motion and (2) detector rotational induced motion. Our novel approach for extracting a respiratory induced motion signal from projection data was based on computing PCA for different sections of the data set independently, and introducing a technique of combining the extracted signal from each section in a manner to represent the respiratory signal from the entire data set. We tested our method using simulation data set from XCAT software and a real patient data set. The respiratory signal extracted with the XCAT simulation yielded comparable result when compared to the ground truth respiratory signal. Initial results for the real patient data set are encouraging but show need for further refinements.