Multi-region tracking for lung tumor motion assessment

There is a need for a method of tracking lung tumors in beam's-eye-view MV image sequences without implanted radiopaque fiducials. We present a multi-region tracking algorithm to follow lung tumors on CT projections and intreatment portal image movies before and during external beam radiotherapy, respectively. Finding suitable landmarks for tracking is challenging due to low contrast in the images. We begin by defining a large set of landmark candidates and a sequence of training images representing the range of tumor motion. Each landmark is found automatically by seeking regions of maximum variance in the image gray values. Small, square templates are centered around each landmark to be used for tracking in sequential MV images. An iterative learning algorithm is employed to select the most suitable templates among the large collection of candidates for the training data set. This subset of templates is then applied to a similar data set for testing. The results of the automatic multi-template selection and tracking compare well to those of manually selected single template tracking. The algorithm shows great promise as a technique for automatically tracking lung tumors in beam's-eye-view in-treatment images without the need for implanted radiopaque fiducials.