Real-time liver tumor localization via a single x-ray projection using deep graph network-assisted biomechanical modeling

Abstract

Real-time imaging is highly desirable in image-guided radiotherapy, as it provides instantaneous knowledge of patient’s anatomy and motion during the treatment and enables online treatment adaptation to achieve the highest tumor targeting accuracy. Due to extremely limited acquisition time, only one or several X-ray projections can be acquired for real-time imaging, which poses a substantial challenge to localize the tumor from the scarce projections. For liver radiotherapy, such a challenge is further exacerbated by the diminished contrast between the tumor and the normal liver tissues. Here, we propose a framework combining graph neural network-based deep learning and biomechanical modeling to track liver tumor in real time from a single on-board X-ray projection. The liver tumor tracking is achieved in two steps. First, a deep learning network is developed to predict the liver surface deformation, using image features learned from the X-ray projection. Second, the intra-liver deformation is estimated through biomechanical modeling, using the liver surface deformation as the boundary condition to solve intra-liver tumor motion by finite element analysis. The accuracy of the proposed framework was evaluated using a dataset of 10 patients with liver cancer. The results show accurate liver surface registration from the graph-based neural network, which translates into accurate real-time, fiducial-less liver tumor localization (<1.3 mm localization error).