Tracking lung tumour motion using a dynamically weighted optical flow algorithm and electronic portal imaging device

This paper investigates the feasibility and accuracy of using a computer vision algorithm and electronic portal images to track the motion of a tumour-like target from a breathing phantom. A multi-resolution optical flow algorithm that incorporates weighting based on the differences between frames was used to obtain a set of vectors corresponding to the motion between two frames. A global value representing the average motion was obtained by computing the average weighted mean from the set of vectors. The tracking accuracy of the optical flow algorithm as a function of the breathing rate and target visibility was investigated. Synthetic images with different contrast-to-noise ratios (CNR) were created, and motions were tracked. The accuracy of the proposed algorithm was compared against potentiometer measurements giving average position errors of 0.6 ± 0.2 mm, 0.2 ± 0.2 mm and 0.1 ± 0.1 mm with average velocity errors of 0.2 ± 0.2 mm s−1, 0.4 ± 0.3 mm s−1 and 0.6 ± 0.5 mm s−1 for 6, 12 and 16 breaths min–1 motions, respectively. The cumulative average position error reduces more rapidly with the greater number of breathing cycles present in higher breathing rates. As the CNR increases from 4.27 to 5.6, the average relative error approaches zero and the errors are less dependent on the velocity. When tracking a tumour on a patient's digitally reconstructed radiograph images, a high correlation was obtained between the dynamically weighted optical flow algorithm, a manual delineation process and a centroid tracking algorithm. While the accuracy of our approach is similar to that of other methods, the benefits are that it does not require manual delineation of the target and can therefore provide accurate real-time motion estimation during treatment.