WE-AB-303-02: A Real-Time Homography-Based Algorithm for Markerless Deformable Lung Tumor Motion Tracking Using KV X-Ray Fluoroscopy




To develop a fast and robust tracking algorithm that does not require implanting fiducial markers for tracking deformable respiration-induced lung tumor motion using kV x-ray fluoroscopy.


Four kV X-ray image sequences were acquired in lung cancer treatment by using On-Board Imaging (OBI) system (Varian Medical Systems, Palo Alto, CA) at frame rate 15 Hz. Given an OBI image sequence, a histogram equalization processing was employed to enhance the tumor contrast in advance. A template image containing the tumor target was then delineated manually in the first frame. Deformable tumor motion in the subsequent frames was represented by a non-linear homographic transformation. The parameters of the homographic transformation were estimated by minimizing a sum-of-squared-difference (SSD) between the template image and the observed frame. To improve the computational efficiency, an efficient second-order minimization (ESM) method was utilized to minimize the SSD.


We evaluated the performance of the proposed method in terms of the tracking accuracy and computational time. For the clinical kV image sequence, the absolute means and standard deviations of tracking error in superior-inferior (SI) and left-right (LR) directions were 0.41±0.43 mm and 0.08±0.29 mm, respectively. The computational time is about 0.04 sec/frame. Experimental results also demonstrated that the proposed method is superior to conventional template matching-based methods both in tracking accuracy and computational cost.


In this study, we developed a novel markerless tracking algorithm to track deformable lung tumor motion. By using the homographic transformation and ESM method, the proposed method is capable of robustly tracking deformable tumor motion in real-time. The experimental results conducted on clinical kV image sequences demonstrated the effectiveness of the proposed method. In addition, compared with conventional template matching-based methods, our method achieved a higher tracking accuracy and lower computational cost.